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The load current of the new power system has significant characteristics on a wide dynamic range, which poses challenges to current sensing technologies. This paper proposes a magnetic-sensitive element based on NdFeB/Lead Zirconate Titanate (PZT) magnetoelectric composite materials, and further develops a magnetoelectric coupling current sensor. The sensor operates in torque mode, enabling the detection of both wide dynamic range alternating currents and weak alternating currents. Experimental studies show that the sensor achieved a power-frequency current detection sensitivity of 15.56 mV/A, a linear range of (0–120) A, and a detection limit of 153 μA. The results indicate that the sensor exhibits high sensitivity in alternating current (AC) current detection, and at power frequency, possesses both a wide dynamic range and the capability to detect weak currents. Therefore, it shows great application potential in scenarios such as wide dynamic range AC current measurement and weak current detection in power systems.

This study focuses on the Dongting Lake region in China and evaluates ecological vulnerability using the Sensitivity–Resilience–Pressure (SRP) framework, integrated with Spatial Principal Component Analysis (SPCA) to calculate the Ecological Vulnerability Index (EVI). The EVI values were classified into five levels using the Natural Breaks (Jenks) method, and spatial autocorrelation analysis was applied to reveal spatial differentiation patterns. The Geodetector model was used to analyze the driving mechanisms of natural and socioeconomic factors on EVI, identifying key influencing variables. Furthermore, the LightGBM algorithm was used for feature optimization, followed by the construction of six machine learning models—Multilayer Perceptron (MLP), Extremely Randomized Trees (ET), Decision Tree (DT), Random Forest (RF), LightGBM, and K-Nearest Neighbors (KNN)—to conduct multi-class classification of ecological vulnerability. Model performance was assessed using ROC–AUC, accuracy, recall, confusion matrix, and Kappa coefficient, and the best-performing model was interpreted using SHAP (SHapley Additive exPlanations). The results indicate that: ① ecological vulnerability increased progressively from the core wetlands and riparian corridors to the transitional zones in the surrounding hills and mountains; ② a significant spatial clustering of ecological vulnerability was observed, with a Moran’s I index of 0.78; ③ Geodetector analysis identified the interaction between NPP (q = 0.329) and precipitation (PRE, q = 0.268) as the dominant factor (q = 0.50) influencing spatial variation of EVI; ④ the Random Forest model achieved the best classification performance (AUC = 0.954, F1 score = 0.78), and SHAP analysis showed that NPP and PRE made the most significant contributions to model predictions. This study proposes a multi-method integrated decision support framework for assessing ecological vulnerability in lake wetland ecosystems.

Background Frailty is prevalent among older adults and exerts a significant impact on their quality of life. The present study aims to analyze the current status of frailty in older adults with chronic obstructive pulmonary disease (COPD) as well as its influencing factors, thereby providing evidence-based support for clinical treatment and nursing practices. Methods Older patients with COPD admitted to our hospital from January 2024 to April 2025 were included in this study. Patients with concurrent asthma or interstitial lung disease (ILD) were excluded to minimize confounding. Clinical data were collected, and frailty was assessed within 24 h of admission (when patients were clinically stable). Correlation analysis and multivariate logistic regression analysis were conducted to identify the influencing factors of frailty in older patients with COPD. Clinical trial number: not applicable. Results A total of 224 older adults with COPD were enrolled in the study, among whom 68 (30.36%) were robust (normal state), 84 (37.50%) were pre-frail, and 72 (32.14%) met the criteria for frailty. Correlation analysis showed that age( r  = 0.602), GOLD stage( r  = 0.596), and COPD duration( r  = 0.558), serum interleukin-6( r  = 0.534) and serum hemoglobin concentration( r =-0.579) were correlated with the frailty of older adults with COPD (all p  < 0.05). Multivariate logistic regression analysis indicated that age (OR = 1.809, 95%CI: 1.233 ~ 2.195), GOLD stage (OR = 2.456, 95%CI: 1.977 ~ 2.850), and COPD duration (OR = 1.768, 95%CI: 1.104 ~ 1.993), serum interleukin-6 (OR = 1.454, 95%CI: 1.113 ~ 2.006) and serum hemoglobin concentration (OR = 0.675, 95%CI: 0.225 ~ 0.901) were the influencing factors of the frailty of older adults with COPD (all p  < 0.05). Conclusion The prevalence of frailty is relatively high in older adults with COPD. Clinically, targeted interventions and nursing care should be carried out for factors that influence the frailty in older adults with COPD.

Colorectal cancer (CRC) is among the most common malignancies worldwide, and advanced or metastatic disease remains difficult to treat because of tumor heterogeneity, adaptive resistance, pathway redundancy, drug-related toxicity, and limited predictive biomarkers. Small-molecule kinase inhibitors (SMKIs) provide therapeutic opportunities for selected molecular subgroups by targeting key signaling pathways, but their clinical application is still constrained by complex resistance mechanisms, off-target toxicity, and insufficient biomarker-guided stratification. This narrative review summarizes recent progress in SMKIs for CRC, including molecular targets, clinical evidence, resistance mechanisms, combination strategies, and translational directions. Emerging technologies, including multi-omics profiling, artificial intelligence-assisted drug discovery, patient-derived models, liquid biopsy, molecular imaging, multidrug delivery systems, and adaptive trial designs, can be integrated into a translational “discover–validate–monitor–adapt” workflow and may help address some current limitations in targeted drug development for CRC. Future development of SMKI-based therapy in CRC will require biomarker-guided patient selection, rational combination strategies, dynamic monitoring of resistance, and prospective validation using clinically meaningful endpoints.

For the application of high-frequency current detection in power systems, such as very fast transient current, lightning current, partial discharge pulse current, etc., current sensors with a quick response are indispensable. Here, we propose a high-frequency magnetoelectric current sensor, which consists of a PZT piezoelectric ceramic and Metglas amorphous alloy. The proposed sensor is designed to work under d15 thickness-shear mode, with the resonant frequency around 1.029 MHz. Furthermore, the proposed sensor is fabricated as a high-frequency magnetoelectric current sensor. A comparative experiment is carried out between the tunnel magnetoresistance sensor and the magnetoelectric sensor, in the aspect of high-frequency current detection up to 3 MHz. Our experimental results demonstrate that the d15 thickness-shear mode magnetoelectric sensor has great potential for high-frequency current detection in smart grids.

Data augmentation diversifies the information in the dataset. For class imbalance, the copy‐paste augmentation generates new class information to alleviate the impact of this problem. However, these methods rely excessively on human intuition. Over‐fitting or under‐fitting can occur while adding the class information, which is inappropriate. The authors propose a self‐adaptive data augmentation: the copy‐paste with self‐adaptation (CPA) algorithm, which improves the phenomenon of over‐fitting and under‐fitting. For the CPA, the evaluation results of a model are taken as an important adjustment basis. The evaluation results are combined with the information of class imbalance to generate a set of class weights. Different number of class information will be replenished according to class weights. Finally, the generated images will be inserted into the training dataset and the model will start formal training. The experimental results show that CPA can alleviate class imbalance. For TT100 K dataset, YOLOv3 is trained with the optimised dataset and its AP is increased by 2% for VOC2007 dataset, the mAP of RetinaNet on optimised dataset is 78.46, which is 1.2% higher than original dataset. For COCO2017 dataset, SSD300 is trained with the optimised dataset and its AP is increased by 1.3%. CPA extracts the evaluation results of the model from pre‐training. Then, the evaluation results are combined with class imbalance to replenish the class information. Finally, the generated images will be inserted into the training dataset and the model will start the formal training.

Increasing attention is focused on the down-regulation of miRNAs in cancer process. Nuclear receptor subfamily 2 （NR2F2, also known as COUP-TFU） is involved in the development of many types of cancers, but its role in gastric cancer remains elusive. In this experiment, oncomine and Kaplan-meier database revealed that NR2F2 was up-regulated in gastric cancer and that the high NR2F2 expression contributed to poor survival. MicroRNA-2Tb was targeted and down-regulated by NR2F2 in human gastric cancer tissues and cells. The ectopic expression of miR-27b inhibited gastric cancer cell proliferation and tumor growth in vitro and in vivo. Assays suggested that the overexpression of miR-27b could promote MGC-803 cells＇ migration and invasion and retard their metastasis to the liver. In addition, down-regulation of miR-27b enhanced GES-1 cells＇ proliferation and metastasis in vitro. These findings reveal that miR-27b is a tumor suppressor in gastric cancer and a biomarker for improving patients＇ survival.

Background Colorectal signet-ring cell carcinoma (SRCC) is a rare cancer with a bleak prognosis. The relationship between its clinicopathological features and survival remains incompletely elucidated. Tumor deposits (TD) have been utilized to guide the N staging in the 8th edition of American Joint Committee on Cancer (AJCC) staging manual, but their prognostic significance remains to be established in colorectal SRCC. Patients and methods The subjects of this study were patients with stage III/IV colorectal SRCC who underwent surgical treatment. The research comprised two cohorts: a training cohort and a validation cohort. The training cohort consisted of 631 qualified patients from the SEER database, while the validation cohort included 135 eligible patients from four independent hospitals in China. The study assessed the impact of TD on Cancer-Specific Survival (CSS) and Overall Survival (OS) using Kaplan-Meier survival curves and Cox regression models. Additionally, a prognostic nomogram model was constructed for further evaluation. Results In both cohorts, TD-positive patients were typically in the stage IV and exhibited the presence of perineural invasion (PNI) ( P  < 0.05). Compared to the TD-negative group, the TD-positive group showed significantly poorer CSS (the training cohort: HR, 1.87; 95% CI, 1.52–2.31; the validation cohort: HR, 2.43; 95% CI, 1.55–3.81; all P values < 0.001). This association was significant in stage III but not in stage IV. In the multivariate model, after adjusting for covariates, TD maintained an independent prognostic value ( P  < 0.05). A nomogram model including TD, N stage, T stage, TNM stage, CEA, and chemotherapy was constructed. Through internal and external validation, the model demonstrated good calibration and accuracy. Further survival curve analysis based on individual scores from the model showed good discrimination. Conclusion TD positivity is an independent factor of poor prognosis in colorectal SRCC patients, and it is more effective to predict the prognosis of colorectal SRCC by building a model with TD and other clinically related variables.

Submerged macrophytes play a key role in the restoration of shallow eutrophic lakes. However, in some subtropical lakes, benthivorous fishes dominate the fish assemblages and influence the growth of submerged plants. A comprehensive understanding of the direct and indirect effects of benthivorous fishes on submerged plants is important. We conducted mesocosm experiments to examine the effects of three densities of benthivorous fish, Misgurnus anguillicaudatus, on the water properties, the growth, asexual reproduction, and the germination of turions of Potamogeton crispus L. Our results showed that fish disturbance increased TN, TP, PO4–P, NH4–N, and NO3–N of the water, raising the extinction coefficient K, Chl a, and the periphyton biomass. Benthivorous fish disturbance reduced the total biomass, root length, relative growth rate (RGR), and branching number while increasing the plant height of P. crispus. The P stoichiometric homeostasis coefficient (HP) (except turions) and HN was lower in plant tissues due to fish disturbance. Benthivorous fish disturbances promoted turions formation (e.g., increased turions total numbers and biomass) of P. crispus. Moreover, P. crispus exhibited transgenerational plasticity for benthivorous fish affecting turion emergence. The maximum final germination rate occurred only when fish density in the mother plant grow experiment matched that in the turion germination experiment. Turions generated by P. crispus disturbed by low‐density fish exhibited increased germination rates. Our findings suggest that controlling benthivorous fish reduces its indirect and direct effects on submerged vegetation, facilitating the successful restoration of these plants. The disturbance caused by benthivorous fish can have long‐lasting detrimental effects on water quality. Not only does it reduce the growth rate of submerged plants, but it also diminishes their capacity for P‐enrichment. Furthermore, the asexual reproduction of plants and germination of offspring are impacted. By improving the management of benthivorous fish, we can aid in the recovery of vegetation.

High-quality ocean in situ profile observations are fundamental for ocean and climate research and operational oceanographic applications. Here we describe a new global ocean subsurface temperature profile database named the Chinese Academy of Science (CAS) Oceanography Data Center version 1 (CODC-v1). This database contains over 17 million temperature profiles between 1940–2023 from all available instruments. The major data source is the World Ocean Database (WOD), but CODC-v1 also includes some data from some Chinese institutes which are not available in WOD. The data are quality-controlled (QC-ed) by a new QC system that considers the skewness of local temperature distributions, topographic barriers, and the shift of temperature distributions due to climate change. Biases in Mechanical Bathythermographs (MBTs), eXpendable Bathythermographs (XBTs), and Bottle data (OSD) are all corrected using recently proposed correction schemes, which makes CODC-v1 a bias-corrected dataset. These aspects ensure the data quality of the CODC-v1 database, making it suitable for a wide spectrum of ocean and climate research and applications.