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Abstract Immunotherapy has shown promising anti-tumor effects across various tumors, yet it encounters challenges from the inhibitory tumor immune microenvironment (TIME). Infiltrating regulatory T cells (Tregs) are important contributors to immunosuppressive TIME, limiting tumor immunosurveillance and blocking effective anti-tumor immune responses. Although depletion or inhibition of systemic Tregs enhances the anti-tumor immunity, autoimmune sequelae have diminished expectations for the approach. Herein, we summarize emerging strategies, specifically targeting tumor-infiltrating (TI)-Tregs, that elevate the capacity of organisms to resist tumors by reprogramming their phenotype. The regulatory mechanisms of Treg reprogramming are also discussed as well as how this knowledge could be utilized to develop novel and effective cancer immunotherapies.

Abstract Aims In an era of evolving diagnostic possibilities, existing diagnostic systems are not fully sufficient to promptly recognize patients with early‐stage hypertrophic cardiomyopathy (HCM) without symptomatic and instrumental features. Considering the sudden death of HCM, developing a novel diagnostic model to clarify the patients with early‐stage HCM and the immunological characteristics can avoid misdiagnosis and attenuate disease progression. Methods and results Three hundred eighty‐five samples from four independent cohorts were systematically retrieved. The weighted gene co‐expression network analysis, differential expression analysis (|log2(foldchange)| > 0.5 and adjusted P

Recent advances in high-throughput proteomic profiling technologies have facilitated the precise quantification of numerous proteins across multiple specimens concurrently. Researchers have the opportunity to comprehensively analyze the molecular signatures in plentiful medical specimens or disease pattern cell lines. Along with advances in data analysis and integration, proteomics data could be efficiently consolidated and employed to recognize precise elementary molecular mechanisms and decode individual biomarkers, guiding the precision treatment of tumors. Herein, we review a broad array of proteomics technologies and the progress and methods for the integration of proteomics data and further discuss how to better merge proteomics in precision medicine and clinical settings.

Abstract Tertiary lymphoid structures (TLS) are clusters of immune cells that resemble and function similarly to secondary lymphoid organs (SLOs). While TLS is generally associated with an anti-tumour immune response in most cancer types, it has also been observed to act as a pro-tumour immune response. The heterogeneity of TLS function is largely determined by the composition of tumour-infiltrating lymphocytes (TILs) and the balance of cell subsets within the tumour-associated TLS (TA-TLS). TA-TLS of varying maturity, density, and location may have opposing effects on tumour immunity. Higher maturity and/or higher density TLS are often associated with favorable clinical outcomes and immunotherapeutic response, mainly due to crosstalk between different proportions of immune cell subpopulations in TA-TLS. Therefore, TLS can be used as a marker to predict the efficacy of immunotherapy in immune checkpoint blockade (ICB). Developing efficient imaging and induction methods to study TA-TLS is crucial for enhancing anti-tumour immunity. The integration of imaging techniques with biological materials, including nanoprobes and hydrogels, alongside artificial intelligence (AI), enables non-invasive in vivo visualization of TLS. In this review, we explore the dynamic interactions among T and B cell subpopulations of varying phenotypes that contribute to the structural and functional diversity of TLS, examining both existing and emerging techniques for TLS imaging and induction, focusing on cancer immunotherapies and biomaterials. We also highlight novel therapeutic approaches of TLS that are being explored with the aim of increasing ICB treatment efficacy and predicting prognosis.

PurposeThe primary cause of pulp and periapical diseases is the invasion of bacteria into the root canal, which results from the continuous destruction of dental hard tissues. Effective management of infections during root canal therapy necessitates effectively irrigation. This study aims to investigate the effects of two antimicrobial peptides (AMPs), buCaTHL4B and Im-4, on root canal biofilms in vitro.MethodsTwo-species biofilms (Enterococcus faecalis and Fusobacterium nucleatum) were selected and anaerobically cultivated. The following treatments were applied: 10 μg/mL buCaTHL4B, 10 μg/mL Im-4, 5 μg/mL buCaTHL4B, 5 μg/mL Im-4, 1 μg/mL buCaTHL4B, 1 μg/mL Im-4, 1% NaOCl, and sterile water. Each group was treated for 3 min. Subsequently, the two strains were co-cultured with 10 μg/mL buCaTHL4B, 10 μg/mL Im-4, 1% NaOCl, and sterile water for 24, 48, and 72 h. The biofilms were examined using confocal laser scanning microscopy (CLSM) with fluorescent staining, and the percentages of dead bacteria were calculated. Quantitative real-time PCR (qRT-PCR) was employed to assess the variations in bacterial proportions during biofilm formation.ResultsCompared to 1% NaOCl, 10 μg/mL buCaTHL4B or Im-4 exhibited significantly greater bactericidal effects on the two-species biofilms (p < 0.05), leading to their selection for subsequent experiments. Over a 48-hour period, 10 μg/mL Im-4 demonstrated a stronger antibiofilm effect than buCaTHL4B (p < 0.05). Following a 24-hour biofilm formation period, the proportion of F. nucleatum decreased while the proportion of E. faecalis increased in the sterile water group. In the buCaTHL4B and 1% NaOCl groups, the proportion of F. nucleatum was lower than that of E. faecalis (p < 0.05), whereas in the Im-4 group, the proportion of F. nucleatum was higher than that of E. faecalis (p < 0.05). The proportions of bacteria in the two AMPs groups gradually stabilized after 24 h of treatment.ConclusionbuCaTHL4B and Im-4 exhibited remarkable antibacterial and anti-biofilm capabilities against pathogenic root canal biofilms in vitro, indicating their potential as promising additives to optimize the effectiveness of root canal treatment as alternative irrigants.

With the rapid increase of wind energy integrated into power systems, wind turbine generators (WTGs) are required to provide frequency support to maintain the system frequency stability. However, the frequency regulation is achieved by employing temporary energy reserves from WTGs at the initial stage of a disturbance. Therefore, a second frequency dip (SFD) may occur, if no other energy reserve is available to compensate the power deficiency as WTGs have to recover their operating points and rotor speeds back to the initial operating points. To deal with this problem, this paper proposes a consecutive power dispatch scheme to reduce the SFD and prevent WTGs from over-deceleration. All WTGs are divided into two groups with in a wind farm: Group 1 (G1) WTGs operating at maximum power point tracking (MPPT), Group 2 (G2) WTGs operating at deloading power. If a frequency contingency occurs, the proposed scheme aims to release an amount of kinetic energy (KE) stored in the rotating masses of G1 WTGs to improve the frequency nadir (FN). Following this, energy reserves are released from G2 WTGs to compensate the power shortage during the period when G1 WTGs rotor speeds have to be recovered. The simulation results show that the scheme causes a small SFD while improving the first FN and preventing the rotor from over-decelerations in various wind conditions, contingency sizes, and wind penetration levels.

Abstract Background Colorectal cancer (CRC) is among the most hackneyed malignancies. Even patients with identical clinical symptoms and the same TNM stage still exhibit radically different clinical outcomes after receiving equivalent treatment regimens, indicating extensive heterogeneity of CRC. Myriad molecular subtypes of CRC have been exploited for decades, including the most compelling consensus molecular subtype (CMS) classification that has been broadly applied for patient stratification and biomarker‐drug combination formulation. Encountering barriers to clinical translation, however, CMS classification fails to fully reflect inter‐ or intra‐tumor heterogeneity of CRC. As a consequence, addressing heterogeneity and precisely managing CRC patients with unique characteristics remain arduous tasks for clinicians. Review In this review, we systematically summarize molecular subtypes of CRC and further elaborate on their clinical applications, limitations, and future orientations. Conclusion In recent years, exploration of subtypes through cell lines, animal models, patient‐derived xenografts (PDXs), organoids, and clinical trials contributes to refining biological insights and unraveling subtype‐specific therapies in CRC. Therapeutic interventions including nanotechnology, clustered regulatory interspaced short palindromic repeat/CRISPR‐associated nuclease 9 (CRISPR/Cas9), gut microbiome, and liquid biopsy are powerful tools with the possibility to shift the immunologic landscape and outlook for CRC precise medicine.

Abstract Background Recently, long non-coding RNAs (lncRNAs) have been demonstrated as essential roles in tumor immune microenvironments (TIME). Nevertheless, researches on the clinical significance of TIME-related lncRNAs are limited in lung adenocarcinoma (LUAD). Methods Single-cell RNA sequencing and bulk RNA sequencing data are integrated to identify TIME-related lncRNAs. A total of 1368 LUAD patients are enrolled from 6 independent datasets. An integrative machine learning framework is introduced to develop a TIME-related lncRNA signature (TRLS). Results This study identified TIME-related lncRNAs from integrated analysis of single‑cell and bulk RNA sequencing data. According to these lncRNAs, a TIME-related lncRNA signature was developed and validated from an integrative procedure in six independent cohorts. TRLS exhibited a robust and reliable performance in predicting overall survival. Superior prediction performance barged TRLS to the forefront from comparison with general clinical features, molecular characters, and published signatures. Moreover, patients with low TRLS displayed abundant immune cell infiltration and active lipid metabolism, while patients with high TRLS harbored significant genomic alterations, high PD-L1 expression, and elevated DNA damage repair (DDR) relevance. Notably, subclass mapping analysis of nine immunotherapeutic cohorts demonstrated that patients with high TRLS were more sensitive to immunotherapy. Conclusions This study developed a promising tool based on TIME-related lncRNAs, which might contribute to tailored treatment and prognosis management of LUAD patients.

Abstract The intricate crosstalk of various cell death forms was recently implicated in cancers, laying a foundation for exploring the association between cell death and cancers. Recent evidence has demonstrated that biological networks outperform snapshot gene expression profiles at discovering promising biomarkers or heterogenous molecular subtypes across different cancer types. In order to investigate the behavioral patterns of cell death-related interaction perturbation in colorectal cancer (CRC), this study constructed the interaction-perturbation network with 11 cell death pathways and delineated four cell death network (CDN) derived heterogeneous subtypes (CDN1-4) with distinct molecular characteristics and clinical outcomes. Specifically, we identified a subtype (CDN4) endowed with high autophagy activity and the worst prognosis. Furthermore, AOC3 was identified as a potential autophagy-related biomarker, which demonstrated exceptional predictive performance for CDN4 and significant prognostic value. Overall, this study sheds light on the complex interplay of various cell death forms and reveals an autophagy-related gene AOC3 as a critical prognostic marker in CRC.

Wet dust removal systems used to control dust in the polishing or grinding process of Mg alloy products are frequently associated with potential hydrogen explosion caused by magnesium-water reaction. For purpose of avoiding hydrogen explosion risks, we try to use a combination of chitosan (CS) and sodium phosphate (SP) to inhibit the hydrogen evolution reaction between magnesium alloy waste dust and water. The hydrogen evolution curves and chemical kinetics modeling for ten different mixing ratios demonstrate that 0.4wt% CS + 0.1wt% SP yields the best inhibition efficiency with hydrogen generation rate of almost zero. SEM and EDS analyses indicate that this composite inhibitor can create a uniform, smooth, tight protective film over the surface of the alloy dust particles. FTIR and XRD analysis of the chemical composition of the surface film show that this protective film contains CS and SP chemically adsorbed on the surface of ZK60 but no detectable Mg(OH)2, suggesting that magnesium-water reaction was totally blocked. Our new method offers a thorough solution to hydrogen explosion by inhibiting the hydrogen generation of magnesium alloy waste dust in a wet dust removal system.

Abstract The reversible oxidation-reduction homeostasis mechanism functions as a specific signal transduction system, eliciting related physiological responses. Disruptions to redox homeostasis can have negative consequences, including the potential for cancer development and progression, which are closely linked to a series of redox processes, such as adjustment of reactive oxygen species (ROS) levels and species, changes in antioxidant capacity, and differential effects of ROS on downstream cell fate and immune capacity. The tumor microenvironment (TME) exhibits a complex interplay between immunity and regulatory cell death, especially autophagy and apoptosis, which is crucially regulated by ROS. The present study aims to investigate the mechanism by which multi-source ROS affects apoptosis, autophagy, and the anti-tumor immune response in the TME and the mutual crosstalk between these three processes. Given the intricate role of ROS in controlling cell fate and immunity, we will further examine the relationship between traditional cancer therapy and ROS. It is worth noting that we will discuss some potential ROS-related treatment options for further future studies.

In the era of marine big data, making full use of multi-source satellite observations to accurately retrieve and predict the temperature structure of the ocean subsurface layer is very significant in advancing the understanding of oceanic processes and their dynamics. Considering the time dependence and spatial correlation of marine characteristics, this study employed the convolutional long short-term memory (ConvLSTM) method to retrieve the subsurface temperature in the Western Pacific Ocean from several types of satellite observations. Furthermore, considering the temperature’s vertical distribution, the retrieved results for the upper layer were iteratively used in the calculation for the deeper layer as input data to improve the algorithm. The results show that the retrieved results for the 100 to 500 m depth temperature using the 50 m layer in the calculation resulted in higher accuracy than those retrieved from the standard ConvLSTM method. The largest improvement was in the calculation for the 100 m layer, where the thermocline was located. The results indicate that our improved ConvLSTM method can increase the accuracy of subsurface temperature retrieval without additional input data.

Abstract Specific emitter identification (SEI) is an emerging device authentication technology, which depends on the inherent hardware characteristics of wireless devices. By analysing the received signal, the hardware characteristics of a specific emitter can be extracted at the receiver and used for device authentication and association. Most of the existing SEI schemes focus on the identification under closed sets. In view of the explosive growth of the number of IoT devices, based on generative adversarial networks, this study proposes a method that considers the identification of unknown emitters. The reconstruction network to reconstruct the signals of the known class and fully train the feature space of the signals of the known class is designed. In the discriminator, two channels are specifically designed to perform anomaly detection for unknown signals and end‐to‐end closed‐set classification for known signals. In order to better reject unknown signals and accept known signals, Receiver operating characteristic curve and Youden index are used to determine the optimal threshold for anomaly detection. Under the given optimal threshold conditions, the identified threshold points have larger True Positive Rate. In addition, the time‐frequency feature combination vector is designed to reveal the essential characteristics of emitters. The experimental results on the real‐world datasets collected from universal software radio peripherals in short‐range communication scenario show that compared with the existing open‐set recognition methods such as C2AE, Openmax and SoftMax, the average recognition accuracy of the proposed framework improved by 0.08, 0.15, and 0.2 respectively, and the Marco‐F1 score improved by 0.05, 0.1, and 0.16, respectively, which proves the superiority of the proposed framework. In addition to identifying unknown and known Universal software radio peripherals in this article, some potential and widespread applications of the proposed framework include access user security authentication in IoT, such as Hack RF and Blue tooth devices. In addition, as a relatively general signal processing framework, the model can be used for air safety management and maritime ship identity authentication in civil aspects. In the military aspect, it can be used in electronic support and various signal reconnaissance scenarios, such as automatic signal modulation recognition in open set scenarios, individual identity determination of radar emitter and unknown working state identification of transmitters, which proves reference for subsequent research on open‐set signal recognition.

Abstract Background Gastric cancer (GC) is one of the most common malignant tumors of the digestive tract which seriously endangers the health of human beings worldwide. Transcriptomic deregulation by epigenetic mechanisms plays a crucial role in the heterogeneous progression of GC. This study aimed to investigate the impact of epigenetically regulated genes on the prognosis, immune microenvironment, and potential treatment of GC. Results Under the premise of verifying significant co-regulation of the aberrant frequencies of microRNA (miRNA) correlated (MIRcor) genes and DNA methylation-correlated (METcor) genes. Four GC molecular subtypes were identified and validated by comprehensive clustering of MIRcor and METcor GEPs in 1521 samples from five independent multicenter GC cohorts: cluster 1 was characterized by up-regulated cell proliferation and transformation pathways, with good prognosis outcomes, driven by mutations, and was sensitive to 5-fluorouracil and paclitaxel; cluster 2 performed moderate prognosis and benefited more from apatinib and cisplatin; cluster 3 was featured by an up-regulated ligand–receptor formation-related pathways, poor prognosis, an immunosuppression phenotype with low tumor purity, resistant to chemotherapy (e.g., 5-fluorouracil, paclitaxel, and cisplatin), and targeted therapy drug (apatinib) and sensitive to dasatinib; cluster 4 was characterized as an immune-activating phenotype, with advanced tumor stages, benefit more from immunotherapy and displayed worst prognosis. Conclusions According to the epigenetically regulated GEPs, we developed four robust GC molecular subtypes, which facilitated the understanding of the epigenetic mechanisms underlying GC heterogeneity, offering an optimized decision-making and surveillance platform for GC patients.

Background Abdominal aortic calcification (AAC) is an independent risk factor for cardiovascular disease. We aim to examine the associations between Life's Essential 8 (LE8), the recently updated measurement of cardiovascular health (CVH), and AAC among participants aged ≥40 years. Methods and Results This population‐based cross‐sectional study used data from the National Health and Nutrition Examination Survey in 2013 to 2014. AAC (AAC score>0) and severe AAC (AAC score>6) were quantified by the Kauppila score system. Multiple linear, multivariable logistic, and restricted cubic spline models were used to assess the associations. A total of 2369 participants were included with a mean AAC score of 1.41 (0.13). Participants in the high‐cardiovascular‐health group had lower AAC scores, lower prevalence of AAC, and lower prevalence of severe AAC. After the adjustment of potential confounders (age, sex, race and ethnicity, education levels, marital status, poverty income ratio, estimated glomerular filtration rate, serum creatinine, serum uric acid, serum phosphorus, and serum total calcium), higher cardiovascular health was significantly associated with lower risk of AAC. Meanwhile, elevated nicotine exposure score, blood glucose score, and blood pressure score within the LE8 components were significantly associated with lower risk of AAC. Also, nonlinear dose–response relationships were observed. Subgroup analyses (age strata, sex, poverty income ratio, education levels, marital status) indicated the inverse associations of LE8 and AAC were generally similar in different populations. Conclusions LE8 was negatively and nonlinearly related to the risk of AAC among middle‐aged and older populations. Meanwhile, LE8 components should prioritize higher scores for nicotine exposure, blood glucose, and blood pressure evaluations.

Background Chronic kidney disease (CKD) is closely associated with cardiovascular disease. We aimed to examine the association of Life's Essential 8 (LE8), the recently updated measurement of cardiovascular health, with the prevalence of CKD among US adults. Methods and Results This population‐based cross‐sectional study used data from the National Health and Nutrition Examination Survey from 2007 to 2018 and included adults aged ≥20 years. Multivariable logistic and restricted cubic spline models were used to assess the associations between LE8 and CKD. Among 24 960 participants, 4437 were determined to have CKD (weighted percentage, 14.11%). After the adjustment of potential confounders, higher LE8 scores were associated with reduced odds of CKD (odds ratio for each 10‐point increase, 0.79 [95% CI, 0.76–0.83]), and a nonlinear dose–response relationship was observed. Similar patterns were also identified in the associations of health behavior and health factor scores with CKD. Meanwhile, higher scores for blood glucose (odds ratio, for each 10‐point increase, 0.88 [95% CI, 0.87–0.90]) and blood pressure (odds ratio, for each 10‐point increase, 0.92 [95% CI, 0.91–0.94]) in the LE8 component are significantly associated with a lower prevalence of CKD. The inversed association of LE8 score and CKD was significantly stronger among middle‐aged, male, and coupled participants. Conclusions LE8 was negatively associated with the prevalence of CKD in a nonlinear fashion. Promoting adherence to optimal cardiovascular health levels may be beneficial to reduce the burden of CKD.

As the main body of resource aggregation, Virtual Power Plant (VPP) not only needs to participate in the external energy market but also needs to optimize the management of internal resources. Different from other energy storage, hydrogen energy storage systems can participate in the hydrogen market in addition to assuming the backup supplementary function of electric energy. For the Virtual Power Plant Operator (VPPO), it needs to optimize the scheduling of internal resources and formulate bidding strategies for the electric-hydrogen market based on external market information. In this study, a two-stage model is constructed considering the internal and external interaction mechanism. The first stage model optimizes the operation of renewable energy, flexible load, extraction storage, and hydrogen energy storage system based on the complementary characteristics of internal resources; the second stage model optimizes the bidding strategy to maximize the total revenue of the electricity energy market, auxiliary service market and hydrogen market. Finally, a typical scenario is constructed and the rationality and effectiveness of the strategy are verified. The results show that the hybrid VPP with hydrogen storage has better economic benefits, resource benefits and reliability.

Doxorubicin is a broad-spectrum chemotherapy drug commonly used in clinical practice. Improving the delivery efficiency of doxorubicin can help improve the therapeutic effect of tumor and reduce the corresponding side effects. The delivery of doxorubicin based on nano-drug loading technology can provide important help for targeted killing of tumor cells. Tumor development is usually accompanied by the changes in the stiffness of extracellular matrix. The effect of substrate stiffness on the endocytic behavior of doxorubicin based on drug-loading nanoparticle technology was explored by the cellular activity regulated by doxorubicin endocytosis and the changes in the expression of important proteins associated with endocytosis. The substrate stiffness can be regulated by adjusting the ratio of PDMS binder and curing agent. The results of the experiment showed that the cell activity on the substrate with appropriate stiffness (Young's modulus of 0.578, 0.815 and 1.986 MPa) was inhibited, indicating that the transport of doxorubicin was more efficient in this range of substrate stiffness. To explore the underlying mechanism of substrate stiffness regulation of doxorubicin endocytosis, we explored the expression of αVβ5 and NUMB, which are closely related to the endocytosis process. From the results of immunofluorescence protein and Western blot experiments, the fluorescence intensity of the αVβ5 protein responsible for anchoring clathrin on the substrate with a Young's modulus of 1.986 MPa was the highest; Within the appropriate stiffness range, the fluorescence intensity of NUMB protein was significantly increased, indicating that increased expression of NUMB and αVβ5 regulated by substrate stiffness mediates doxorubicin transport, thereby affecting cell viability. This work provides a reference for further improving the efficacy of chemotherapy drugs in the treatment of tumors.

Background: Leiomyomatosis peritonealis disseminata (LPD) is a non-metastatic, homologous, multicentric benign disorder characterized by small leiomyomas scattered over the peritoneum and omentum. It is a rare and benign disease with invasive potential. LPD mainly attacks women of childbearing age but has also been reported in post-menopausal women, men, and young children. Non-specific clinical and imaging findings of LPD lead to difficult diagnoses and treatment. Case presentation: This study reports the case of a patient with recurrent LPD with endometriosis after multiple myomectomies and hysterectomy, who presented recurrent abdominal pain with progressive exacerbation. Imaging examinations showed irregular shadows in the pelvic cavity and multiple nodular changes in the peritoneum, which were considered malignant lesions. A solid mass sized 10 cm × 9 cm × 10 cm in the inferior pelvis and nodules scattered over the surface of pelvic and abdominal organs and the peritoneum were detected during the surgery. The patient was treated with cytoreductive surgery (CRS), peritonectomy, ovarian ablation, and hyperthermic intraperitoneal chemotherapy (HIPEC). The surgery was challenging, and the intraoperative bleeding reached 900 ml. However, the patient recovered well and achieved a tumor-free survival of 13 months. Conclusions: It was concluded that a combination of CRS, ovarectomy, and HIPEC might be one of the therapeutic strategies for recurrent LPD.

Abstract Cancer drug resistance represents the main obstacle in cancer treatment. Drug-resistant cancers exhibit complex molecular mechanisms to hit back therapy under pharmacological pressure. As a reversible epigenetic modification, N6-methyladenosine (m6A) RNA modification was regarded to be the most common epigenetic RNA modification. RNA methyltransferases (writers), demethylases (erasers), and m6A-binding proteins (readers) are frequently disordered in several tumors, thus regulating the expression of oncoproteins, enhancing tumorigenesis, cancer proliferation, development, and metastasis. The review elucidated the underlying role of m6A in therapy resistance. Alteration of the m6A modification affected drug efficacy by restructuring multidrug efflux transporters, drug-metabolizing enzymes, and anticancer drug targets. Furthermore, the variation resulted in resistance by regulating DNA damage repair, downstream adaptive response (apoptosis, autophagy, and oncogenic bypass signaling), cell stemness, tumor immune microenvironment, and exosomal non-coding RNA. It is highlighted that several small molecules targeting m6A regulators have shown significant potential for overcoming drug resistance in different cancer categories. Further inhibitors and activators of RNA m6A-modified proteins are expected to provide novel anticancer drugs, delivering the therapeutic potential for addressing the challenge of resistance in clinical resistance.

To accurately predict the prognosis and further improve the clinical outcomes of bladder cancer (BLCA), we leveraged large‐scale data to develop and validate a robust signature consisting of small gene sets. Ten machine‐learning algorithms were enrolled and subsequently transformed into 76 combinations, which were further performed on eight independent cohorts (n = 1218). We ultimately determined a consensus artificial intelligence‐derived gene signature (AIGS) with the best performance among 76 model types. In this model, patients with high AIGS showed a higher risk of mortality, recurrence, and disease progression. AIGS is not only independent of traditional clinical traits [(e.g., American Joint Committee on Cancer (AJCC) stage)] and molecular features (e.g., TP53 mutation) but also demonstrated superior performance to these variables. Comparisons with 58 published signatures also indicated that AIGS possessed the best performance. Additionally, the combination of AIGS and AJCC stage could achieve better performance. Patients with low AIGS scores were sensitive to immunotherapy, whereas patients with high AIGS scores might benefit from seven potential therapeutics: BRD‐K45681478, 1S,3R‐RSL‐3, RITA, U‐0126, temsirolimus, MRS‐1220, and LY2784544. Additionally, some mutations (TP53 and RB1), copy number variations (7p11.2), and a methylation‐driven target were characterized by AIGS‐related multi‐omics alterations. Overall, AIGS provides an attractive platform to optimize decision‐making and surveillance protocol for individual BLCA patients.

Background: Patients who survive initial esophagogastric variceal bleeding (EVB) are at an increased risk of recurrent bleeding and death; however, a reliable predictive model is lacking. We aimed to develop a model for rebleeding prediction in patients with EVB after modified percutaneous transhepatic variceal embolization (PTVE) with cyanoacrylate. Methods: A total of 122 patients with EVB who underwent PTVE from January 2015 to November 2020 were enrolled. Multivariate logistic analyses were conducted to determine independent risk factors for nomogram construction. The discrimination, calibration, and clinical utility of the nomogram were compared with the Model for End-stage Liver Disease score (MELD) and the Child–Pugh model. Risk stratification was performed according to the nomogram. Results: Rebleeding within 3 months of PTVE occurred in 32 patients (26.2%). Independent rebleeding indicators included prior history of endoscopic therapy, Child–Pugh score, partial splenic embolization, and creatinine level. The nomogram incorporating these four predictors achieved excellent calibration and discriminatory abilities, with a concordance index of 0.85, which was confirmed to be 0.83 through bootstrapping validation. The nomogram demonstrated superior discrimination and clinical applicability than the MELD and Child–Pugh models. As shown in the Kaplan–Meier curves, high-risk patients had a high probability of rebleeding (P ​

Identification of long non-coding RNA (lncRNA) signatures could be used to improve cancer clinical outcome. Here the authors developed a machine learning-based integrative procedure to construct a consensus immune-related lncRNA signature to predict prognosis, recurrence and treatment benefits in colorectal cancer.

SMAD4 mutation was recently implicated in promoting invasion and poor prognosis of pancreatic cancer (PACA) by regulating the tumor immune microenvironment. However, SMAD4-driven immune landscape and clinical significance remain elusive. In this study, we applied the consensus clustering and weighted correlation network analysis (WGCNA) to identify two heterogeneous immune subtypes and immune genes. Combined with SMAD4-driven genes determined by SMAD4 mutation status, a SMAD4-driven immune signature (SDIS) was developed in ICGC-AU2 (microarray data) via machine learning algorithm, and then was validated by RNA-seq data (TCGA, ICGC-AU and ICGC-CA) and microarray data (GSE62452 and GSE85916). The high-risk group displayed a worse prognosis, and multivariate Cox regression indicated that SDIS was an independent prognostic factor. In six cohorts, SDIS also displayed excellent accuracy in predicting prognosis. Moreover, the high-risk group was characterized by higher frequencies of TP53/CDKN2A mutations and SMAD4 deletion, superior immune checkpoint molecules expression and more sensitive to chemotherapy and immunotherapy. Meanwhile, the low-risk group was significantly enriched in metabolism-related pathways and suggested the potential to target tumor metabolism to develop specific drugs. Overall, SDIS could robustly predict prognosis in PACA, which might serve as an attractive platform to further tailor decision-making in chemotherapy and immunotherapy in clinical settings.

Background: Pharmacogenomics is crucial for individualized drug therapy and plays an increasingly vital role in precision medicine decision-making. However, pharmacogenomics-based molecular subtypes and their potential clinical significance remain primarily unexplored in lung adenocarcinoma (LUAD). Methods: A total of 2065 samples were recruited from eight independent cohorts. Pharmacogenomics data were generated from the profiling of relative inhibition simultaneously in mixtures (PRISM) and the genomics of drug sensitivity in cancer (GDSC) databases. Multiple bioinformatics approaches were performed to identify pharmacogenomics-based subtypes and find subtype-specific properties. Results: Three reproducible molecular subtypes were found, which were independent prognostic factors and highly associated with stage, survival status, and accepted molecular subtypes. Pharmacogenomics-based subtypes had distinct molecular characteristics: S-Ⅰ was inflammatory, proliferative, and immune-evasion; S-Ⅱ was proliferative and genetics-driven; S-III was metabolic and methylation-driven. Finally, our study provided subtype-guided personalized treatment strategies: Immune checkpoint blockers (ICBs), doxorubicin, tipifarnib, AZ628, and AZD6244 were for S-Ⅰ; Cisplatin, camptothecin, roscovitine, and A.443654 were for S-Ⅱ; Docetaxel, paclitaxel, vinorelbine, and BIBW2992 were for S-III. Conclusion: We provided a novel molecular classification strategy and revealed three pharmacogenomics-based subtypes for LUAD patients, which uncovered potential subtype-related and patient-specific therapeutic strategies.

BackgroundMolecular biomarkers are widely used for disease diagnosis and exploration of pathogenesis. Pulmonary arterial hypertension (PAH) is a rapidly progressive cardiopulmonary disease with delayed diagnosis. Studies were limited regarding molecular biomarkers correlated with PAH from a broad perspective.MethodsTwo independent microarray cohorts comprising 73 PAH samples and 36 normal samples were enrolled in this study. The weighted gene co-expression network analysis (WGCNA) was performed to identify the key modules associated with PAH. The LASSO algorithm was employed to fit a diagnostic model. The latent biology mechanisms and immune landscape were further revealed via bioinformatics tools.ResultsThe WGCNA approach ultimately identified two key modules significantly associated with PAH. For genes within the two models, differential expression analysis between PAH and normal samples further determined nine key genes. With the expression profiles of these nine genes, we initially developed a PAH diagnostic signature (PDS) consisting of LRRN4, PI15, BICC1, PDE1A, TSHZ2, HMCN1, COL14A1, CCDC80, and ABCB1 in GSE117261 and then validated this signature in GSE113439. The ROC analysis demonstrated outstanding AUCs with 0.948 and 0.945 in two cohorts, respectively. Besides, patients with high PDS scores enriched plenty of Th17 cells and neutrophils, while patients with low PDS scores were dramatically related to mast cells and B cells.ConclusionOur study established a robust and promising signature PDS for diagnosing PAH, with key genes, novel pathways, and immune landscape offering new perspectives for exploring the molecular mechanisms and potential therapeutic targets of PAH.

BackgroundThe unknown etiology of sarcoidosis with variable clinical features leads to delayed diagnosis and limited therapeutic strategies. Hence, exploring the latent mechanisms and constructing an accessible and reliable diagnostic model of sarcoidosis is vital for innovative therapeutic approaches to improve prognosis.MethodsThis retrospective study analyzed transcriptomes from 11 independent sarcoidosis cohorts, comprising 313 patients and 400 healthy controls. The weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) analysis were performed to identify molecular biomarkers. Machine learning was employed to fit a diagnostic model. The potential pathogenesis and immune landscape were detected by bioinformatics tools.ResultsA 10-gene signature SARDS consisting of GBP1, LEF1, IFIT3, LRRN3, IFI44, LHFPL2, RTP4, CD27, EPHX2, and CXCL10 was further constructed in the training cohorts by the LASSO algorithm, which performed well in the four independent cohorts with the splendid AUCs ranging from 0.938 to 1.000. The findings were validated in seven independent publicly available gene expression datasets retrieved from whole blood, PBMC, alveolar lavage fluid cells, and lung tissue samples from patients with outstanding AUCs ranging from 0.728 to 0.972. Transcriptional signatures associated with sarcoidosis revealed a potential role of immune response in the development of the disease through bioinformatics analysis.ConclusionsOur study identified and validated molecular biomarkers for the diagnosis of sarcoidosis and constructed the diagnostic model SARDS to improve the accuracy of early diagnosis of the disease.

To understand influenza vaccination and its correlates among health-careworkers (HCWs) during the 2019/2020 season in China, we used a self-administeredelectronic questionnaire to collect information on demographics, occupational characteristics, influenza vaccination status and access to free vaccination on the “Breath Circles”, a Chinese media platform for respiratory medical professionals. The reported influenza vaccine coverage among HCWs during this season was 67%, with more HCWs in a workplace with free vaccination than those with no free vaccination (79% vs.34%,p < .001). The influenza vaccine coverage among HCWs who were required or encouraged to get vaccinated by the workplace was significantly higher than that without any intervention measures (80% & 70 vs.39%,p < .001). The vaccine coverage in the workplaces with free and required vaccination simultaneously was highest compared to that with neither free vaccination nor any intervention measures (OR = 14.86, 95% CI: 10.93–20.20). The influenza vaccination coverage of HCWs in high-riskdepartments was significantly higher than that of other departments (70% vs.58%,p =.023). HCWs’ vaccine coverage was related to personal opinions and attitudes toward influenza or influenza vaccines, as well as other constraints such as availability of influenza vaccines, workplace regulations, and access to free vaccines.

Dental caries is a major public health problem. Streptococcus mutans (S. mutans) are the main etiologic pathogens in dental caries and cause tooth hard tissue demineralization. To prevent dental caries, it is important to inhibit S. mutans and induce tooth remineralization. In this study, we combined a self-assembled lysozyme with polyphemusin I (PI) to form a multi-functional membrane for inhibiting S. mutans and inducing remineralization. The normal enamel coated with lysozyme nanofilm could adsorb significant more PI. After combining with PI, the coated enamel could kill more S. mutans and had the superior ability in inhibiting biofilm formation. In addition, the coated enamel combined with PI could prevent the loss of calcium and phosphate ions. In the pH cycling, the coated enamel regenerated more crystals than normal enamel did. The coated enamel combined with PI had a significant lower lesion depth (72.45 ± 4.07 μm) compared to normal enamel combined with PI (93.30 ± 7.64 μm) after S. mutans demineralization. Moreover, the lysozyme nanofilm and PI had good biocompatibility. Accordingly, the formed multi-functional membrane could be a promising strategy for preventing dental caries.

BackgroundFluorouracil (FU)-based chemotherapy regimens are indispensable in the comprehensive treatment of colorectal cancer (CRC). However, the heterogeneity of treated individuals and the severe adverse effects of chemotherapy results in limited overall benefit.MethodsFirstly, Weighted gene co-expression network analysis (WGCNA) identified modules tightly associated with chemotherapy response. Then, the in-house cohort and prognostic cohorts from TCGA and GEO were subjected to Cox proportional hazards model and survival analysis to ascertain the predictable function of SCG2 on the prognosis of CRC patients. Finally, we performed In vitro experiments, functional analysis, somatic mutation, and copy number variation research to explore the biological characteristics of SCG2.ResultsWe identified red and green as the modules most associated with chemotherapy response, in which SCG2 was considered a risky factor with higher expression predicting poorer prognosis. SCG2 expression in the APC non-mutation group was remarkably higher than in the mutation group. The mutation frequencies of amplified genes differed significantly between different SCG2 expression subgroups. Besides, CRC cell lines with SCG2 knockdown have reduced invasive, proliferative, and proliferative capacity. We discovered that the SCG2 high expression subgroup was the immune hot type and considered more suitable for immunotherapy.ConclusionThis study demonstrates the clinical significance and biological characteristics of SCG2, which could serve as a promising biomarker to identify patients who may benefit from chemotherapy and immunotherapy.

PurposeThe study aimed to construct and evaluate a CT-Based radiomics model for noninvasive detecting perineural invasion (PNI) of perihilar cholangiocarcinoma (pCCA) preoperatively.Materials and MethodsFrom February 2012 to October 2021, a total of 161 patients with pCCA who underwent resection were retrospectively enrolled in this study. Patients were allocated into the training cohort and the validation cohort according to the diagnostic time. Venous phase images of contrast-enhanced CT were used for radiomics analysis. The intraclass correlation efficient (ICC), the correlation analysis, and the least absolute shrinkage and selection operator (LASSO) regression were applied to select radiomics features and built radiomics signature. Logistic regression analyses were performed to establish a clinical model, a radiomics model, and a combined model. The performance of the predictive models was measured by area under the receiver operating characteristic curve (AUC), and pairwise ROC comparisons between models were tested using the Delong method. Finally, the model with the best performance was presented as a nomogram, and its calibration and clinical usefulness were assessed.ResultsFinally, 15 radiomics features were selected to build a radiomics signature, and three models were developed through logistic regression. In the training cohort, the combined model showed a higher predictive capability (AUC = 0.950) than the radiomics model and the clinical model (AUC: radiomics = 0.914, clinical = 0.756). However, in the validation cohort, the AUC of the radiomics model (AUC = 0.885) was significantly higher than the other two models (AUC: combined = 0.791, clinical = 0.567). After comprehensive consideration, the radiomics model was chosen to develop the nomogram. The calibration curve and decision curve analysis (DCA) suggested that the nomogram had a good consistency and clinical utility.ConclusionWe developed a CT-based radiomics model with good performance to noninvasively predict PNI of pCCA preoperatively.

Abstract Background The tumor immunological microenvironment (TIME) has a prominent impact on prognosis and immunotherapy. However, the heterogeneous TIME and the mechanisms by which TIME affects immunotherapy have not been elucidated in hepatocellular carcinoma (HCC). Methods A total of 2195 eligible HCC patients from TCGA and GEO database were collected. We comprehensively explored the different heterogeneous TIME phenotypes and its clinical significance. The potential immune escape mechanisms and what genomic alterations may drive the formation of different phenotypes were further investigated. Results We identified three phenotypes in HCC: TIME-1, the “immune-deficiency” phenotype, with immune cell depletion and proliferation; TIME-2, the “immune-suppressed” phenotype, with enrichment of immunosuppressive cells; TIME-3, the “immune-activated phenotype”, with abundant leukocytes infiltration and immune activation. The prognosis and sensitivity to both sorafenib and immunotherapy differed among the three phenotypes. We also underlined the potential immune escape mechanisms: lack of leukocytes and defective tumor antigen presentation capacity in TIME-1, increased immunosuppressive cells in TIME-2, and rich in immunoinhibitory molecules in TIME-3. The different phenotypes also demonstrated specific genomic events: TIME-1 characterized by TP53, CDKN2A, CTNNB1, AXIN1 and FOXD4 alterations; TIME-2 characterized by significant alteration patterns in the PI3K pathway; TIME-3 characterized by ARID1A mutation. Besides, the TIME index (TI) was proposed to quantify TIME infiltration pattern, and it was a superior prognostic and immunotherapy predictor. A pipeline was developed to classify single patient into one of these three subtypes and calculated the TI. Conclusions We identified three TIME phenotypes with different clinical outcomes, immune escape mechanisms and genomic alterations in HCC, which could present strategies for improving the efficacy of immunotherapy. TI as a novel prognostic and immunotherapeutic signature that could guide personalized immunotherapy and clinical management of HCC.

Automatic signal recognition (ASR) is becoming increasingly important in spectrum identification and cognitive radio, but most existing space-time block code (STBC) recognition algorithms are traditional ones and do not account for the complementarities between different features. To overcome these deficiencies, a multi-delay features fusion scheme for ASR of STBC using a convolutional neural network (CNN) is proposed in this study. The proposed scheme tries to fuse different time-delay features of received STBC signals to obtain more discriminating features. The dilated convolution of different dilation rates is applied to realize automated multi-delay feature extraction. Then, two fusion methods, i.e., max-correlation (MC) fusion and multi-delay average (MDA) fusion, are proposed to combine the features of different time delays, and a residual block is applied to achieve better representation of signals. Finally, the simulation results demonstrate the superior performance of the proposed method. It is notable that the recognition accuracy can reach 97.4% with a signal-to-noise ratio (SNR) of −5 dB. In addition, the proposed multi-delay features fusion CNN (MDFCNN) scheme does not need a priori information, i.e., modulation type, channel coefficients, and noise power, which is well suited to non-collaborative communication.

Low back pain (LBP), which is one of the most severe medical and social problems globally, has affected nearly 80% of the population worldwide, and intervertebral disc degeneration (IDD) is a common musculoskeletal disorder that happens to be the primary trigger of LBP. The pathology of IDD is based on the impaired homeostasis of catabolism and anabolism in the extracellular matrix (ECM), uncontrolled activation of immunologic cascades, dysfunction, and loss of nucleus pulposus (NP) cells in addition to dynamic cellular and biochemical alterations in the microenvironment of intervertebral disc (IVD). Currently, the main therapeutic approach regarding IDD is surgical intervention, but it could not considerably cure IDD. Exosomes, extracellular vesicles with a diameter of 30–150 nm, are secreted by various kinds of cell types like stem cells, tumor cells, immune cells, and endothelial cells; the lipid bilayer of the exosomes protects them from ribonuclease degradation and helps improve their biological efficiency in recipient cells. Increasing lines of evidence have reported the promising applications of exosomes in immunological diseases, and regarded exosomes as a potential therapeutic source for IDD. This review focuses on clarifying novel therapies based on exosomes derived from different cell sources and the essential roles of exosomes in regulating IDD, especially the immunologic strategy.

ObjectiveClinical trials have recently shown a connection between nonalcoholic fatty liver disease (NAFLD) and empagliflozin. This paper aimed at comprehensively assessing the effectiveness and security of empagliflozin in NAFLD patients.MethodsPubMed, Embase, Web of Science, Cochrane Library, CNKI, CBM, Wan-Fang digital database, VIP, and WHO ICTRP were searched for randomized controlled trials (RCTs) on the role of empagliflozin in NAFLD from inception to November 2, 2021. For continuous dating, we used values of mean differences (MD) to present.ResultsA total of four articles involving 244 NAFLD patients were included. Compared with the control group, empagliflozin could significantly reduce the body mass index (BMI) (MD: −0.98 [95% CI: −1.87, −0.10], p = 0.03), liver stiffness measurement (LSM) (MD: 0.49 [95% CI: −0.93, −0.06], p = 0.03), aspartate aminotransferase (AST) (MD: −3.10 [95% CI: −6.18, −0.02], p = 0.05), homeostasis model assessment of insulin resistance (HOMA-IR) (MD: −0.45 [95% CI: −0.90, 0.00], p = 0.05) of the treatment group.ConclusionsEmpagliflozin can improve body composition, insulin resistance, and liver fibrosis and decrease the hepatic enzymes in patients with NAFLD. Empagliflozin emerges as a new option for treating patients with NAFLD. However, further research shall determine the efficacy and safety of empagliflozin in NAFLD.

Background: Aldehyde dehydrogenase 2 (ALDH2) is well-known to be a key enzyme in alcohol metabolism. However, a comprehensive understanding of ALDH2 across human cancers is lacking.Methods: A systematic and comprehensive analysis of the molecular alterations and clinical relevance for ALDH2 in more than 10,000 samples from 33 cancer types was performed. qRT-PCR was performed on 60 cancer and 60 paired nontumor tissues.Results: It was observed that ALDH2 was generally downregulated in most cancers, which was mainly driven by DNA hypermethylation rather than mutations or copy number variations. Besides, ALDH2 was closely related to the inhibition and activation of tumor pathways and a variety of potential targeted agents had been discovered in our research. Last but not least, ALDH2 had the best prediction efficacy in assessing immunotherapeutic response compared with PD-L1, PD-1, CTLA4, CD8, and tumor mutation burden (TMB) in cutaneous melanoma. According to the analysis of large-scale public data and 60 pairs of clinical cancer samples, we found the downregulation of ALDH2 expression tends to suggest the malignant phenotypes and adverse prognosis, which might enhance the precise diagnosis and timely intervention of cancer patients.Conclusion: This study advanced the understanding of ALDH2 across cancers, and provided important insight into chemotherapy, immunotherapy and prognosis of patients with cancer.

In this study, a typical site that had been contaminated by the chemical industry in the past was selected for investigation. To reveal the pollution status, 18 organochlorine pesticides (OCPs) and seven polychlorinated biphenyls (PCBs) in the surface soil and sediment samples were analysed by gas chromatography-mass spectrometry (GC-MS). The levels of the persistent organochlorine residues in the sediment ranged from 44.59-143.29 ng/g, whereas they were 13.94-97.91 ng/g in the soil. A principal component analysis identified that PCBs and dichlorodiphenyltrichloroethanes (DDTs) were the primary sources of the organochlorine residues in the study area. The p,p'-DDT/(p,p'-DDE + p,p'-DDD) values were all below 1, which indicated that the historical inputs of the technical DDT and dicofol were the major sources of DDTs rather than recent inputs. The composition diversity of the hexachlorocyclohexane (HCH) isomers showed that the main sources in the soil were the past use of pesticides, whereas the sediment sources might be due to the historical use of technical HCHs and recent lindane inputs. The ecological risk assessment showed that γ-HCH could pose the highest risks for benthic organisms, followed by DDTs and heptachlor epoxide. These compounds are known to bio-accumulate in fatty tissues. Therefore, routine monitoring of the persistent organochlorine residues in the area is needed and the health risks to local residents should be assessed.

Abstract Background Existing studies reporting on the levels of physical fitness among college students used relatively few fitness tests as a reflection of physical fitness, which could not comprehensively evaluate the levels of physical fitness. Thus, the current study aimed to investigate the cross-sectional relationship between body mass index (BMI) and a physical fitness index (PFI) based on six indicators of fitness in Chinese college students. Method Anthropometric measurements and six measures of physical fitness (Vital capacity, 50-m sprint, sit and reach, standing long jump, 800/1000-m run, pull-up/bent-leg sit-up) were measured. BMI was calculated to classify individuals into underweight, normal weight, overweight, and obesity groups. Z-scores based on sex-specific mean and standard deviation were calculated, and the sum of z-scores for the six fitness tests was used as a PFI. Three models (a linear regression model, polynomial regression model with a second-order BMI term and a restricted cubic spline regression model) were fitted to discuss the potential relation between BMI and PFI. We compared the models using Akaike Information Criterion (AIC) and R square. Results Totally, 8548 freshmen from the years 2014 to 2016 in a medical college completed the physical fitness tests. There was a decreasing trend of physical fitness index from the years 2014 to 2016 (P for trend

Abstract Diverting agents that could plug a targeted location and fill different shapes of fractures have attracted an increasing attention in the field of fracturing. In this study, a series of novel diverting agents were prepared by mixing aldehyde sodium alginate (ASA) and amino gelatin (AG) through a Schiff base reaction to form a primary network, polylactic acid (PLA) fibers were added to produce a 3D network, and Ca2+ was mixed in as an ionic cross‐linking agent to strengthen the cross‐linking points. It was found that gelation time, swelling behavior, and mechanical performance could be controlled by varying the content of aldehyde groups and amino groups. Gelation time could be controlled in 1‐10 minutes. Plugging behavior and degradation tests demonstrated that the hydrogel could suit fractures with various shapes and plug effectively under high temperatures and pressures, and it could eventually become acidic fluid to help clean the wellbore. This novel ASA/AG/PLA hydrogel constitutes a promising environmentally friendly diverting agent in the application of diversion fracturing.

MicroRNAs (miRNAs) were recently implicated in modifying the transforming growth factor β (TGF-β) signaling in multiple cancers. However, TGF-β-derived miRNAs and their potential clinical significance remain largely unexplored in intrahepatic cholangiocarcinoma (ICC). In this study, we proposed an integrated framework that enables the identification of TGF-β-derived miRNAs in ICC (termed “TGFmitor”). A total of 36 TGF-β-derived miRNAs were identified, of which nine significantly correlated with overall survival (OS) and aberrantly expressed in ICC. According to these miRNAs, we discovered and validated a TGF-β associated miRNA signature (TAMIS) in GSE53870 (n =63) and TCGA-CHOL (n =32). To further confirm the clinical interpretation of TAMIS, another validation based on qRT-PCR results from 181 ICC tissues was performed. TAMIS was proven to be an independent risk indicator for both OS and relapse-free survival (RFS). TAMIS also displayed robust performance in three cohorts, with satisfactory AUCs and C-index. Besides, patients with low TAMIS were characterized by superior levels of CD8+ T cells infiltration and PD-L1 expression, while patients with high TAMIS possessed enhanced CMTM6 expression. Kaplan-Meier analysis suggested CMTM6 could further stratify TAMIS. The TAMIShighCMTM6high subtype had the worst prognosis and lowest levels of CD8A and PD-L1 expression relative to the other subtypes, indicating this subtype might behave as “super-cold” tumors. Notably, the improved discrimination was observed when CMTM6 was combined with TAMIS. Overall, our signature could serve as a powerful tool to help improve prognostic management and immunotherapies of ICC patients.

In the Bering Sea slope, ocean eddies are essential physical processes that carry nutrients to the shelf. The development of the satellite altimeter has facilitated the observation of oceanic eddies. Attention networks are used as the core algorithm for eddy detection to suppress feature responses in irrelevant non-eddy areas, which can address the issue of sample imbalance in high-latitude ocean eddies. Furthermore, data from both the sea surface height (SSH) and geostrophic velocity were employed as model inputs to integrate more eddy-related properties. The results of ocean eddy detection using this method and the dataset allowed more eddies to be detected than with traditional vector geometry-based methods and only SSH-based models. This study also incorporated the results of multiple deep learning models to increase both the overall and single-day eddy detection efficiency. As a result, the algorithms in this paper show that attention networks and geostrophic velocity data are both appropriate for high-latitude ocean eddy identification. This makes a contribution to the further application of deep learning methods to satellite altimetry data.