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In this research, a compact dual-band dual-antenna system with mutual coupling reduction based on characteristic mode theory and surface current distribution for 5G mobile terminals is proposed. The edge-to-edge distance between two antennas is only 5 mm. The dimensions of the antenna system are 6 mm × 34 mm (approximately 0.07λmin × 0.41λmin, where λmin corresponds to the lowest operational frequency). The design comprises two modified inverted-F antenna elements with parasitic feeding, which are arranged in a back-to-back configuration. A defected ground structure (DGS) and a neutralization line (NL) are employed for decoupling in the lower (3.3–3.6 GHz) and upper (5.1–5.9 GHz) bands, respectively, achieving isolation levels of |S₂₁|< –25.5 dB and < –23.3 dB. The proposed system demonstrates compact geometry, low-cost fabrication, and scalability for array applications . This study presents a systematic design methodology that integrates characteristic mode analysis with current distribution optimization. The proposed approach enables direct translation of current distributions into physical antenna parameters, effectively eliminating empirical trial-and-error processes while establishing a reproducible design paradigm for future antenna development.

To address the issues of single-structured feature input channels, insufficient feature learning capabilities in noisy environments, and large model parameter sizes in intelligent diagnostic models for mechanical equipment, a lightweight and efficient multimodal feature fusion convolutional neural network (LEMFN) method is proposed. Compared with existing models, LEMFN captures rich fault features at multiple scales by combining time-domain and frequency-domain signals, thereby enhancing the model’s robustness to noise and improving data adaptability under varying operating conditions. Additionally, the convolutional block attention module (CBAM) and random overlapping sampling technology (ROST) are introduced, and through a feature fusion strategy, the accurate diagnosis of mechanical equipment faults is achieved. Experimental results demonstrate that the proposed method not only possesses high diagnostic accuracy and rapid convergence but also exhibits strong robustness in noisy environments. Finally, a graphical user interface (GUI)-based mechanical equipment fault detection system was developed to promote the practical application of intelligent fault diagnosis in mechanical equipment.

Convolutional Neural Networks have been widely applied in fault diagnosis tasks of mechanical systems due to their strong feature extraction and classification capabilities. However, they have limitations in handling global context information. Vision Transformers, by leveraging self-attention mechanisms to capture global dependencies, have shown excellent performance in many visual tasks, but often come with high computational costs. Therefore, this paper proposes a lightweight and efficient intelligent fault diagnosis method based on the fusion of Convolutional Network and Vision Transformer features (FCNVT). This method combines the local feature extraction capability of CNNs with the global dependency capturing ability of ViTs, while maintaining computational efficiency. Random overlapping sampling (ROS) techniques are used to preprocess signals, generating two-dimensional synchronized wavelet transform (SWT) images as inputs to the network. Experimental verification has shown that the proposed method achieves up to 100% classification accuracy, with the model having 7 million parameters and a computational cost of only 0.28 G, outperforming other state-of-the-art methods. Finally, a graphical user interface (GUI)-based mechanical equipment fault detection system was developed using this method, which holds positive implications for advancing the practical application of intelligent fault diagnosis in mechanical equipment.

Ovarian cancer (OC) is characterized by frequent widespread peritoneal metastasis. Cancer‐associated fibroblasts (CAFs) represent a critical stromal component of metastatic niche and promote omentum metastasis in OC patients. However, the role of exosomes derived from omental CAFs in metastasis remains unclear. We isolated exosomes from primary omental normal fibroblasts (NFs) and CAFs from OC patients (NF‐Exo and CAF‐Exo, respectively) and assessed their effect on metastasis. In mice bearing orthotopic OC xenografts, CAF‐Exo treatment led to more rapid intraperitoneal tumor dissemination and shorter animal survival. Similar results were observed in mice undergoing intraperitoneal injection of tumor cells. Among the miRNAs downregulated in CAF‐Exo, miR‐29c‐3p in OC tissues was associated with metastasis and survival in patients. Moreover, increasing miR‐29c‐3p in CAF‐Exo significantly weakened the metastasis‐promoting effect of CAF‐Exo. Based on RNA sequencing, expression assays, and luciferase assays, matrix metalloproteinase 2 (MMP2) was identified as a direct target of miR‐29c‐3p. These results verify the significant contribution of exosomes from omental CAFs to OC peritoneal metastasis, which could be partially due to the relief of MMP2 expression inhibition mediated by low exosomal miR‐29c‐3p. The conversion from NFs to CAFs causes a reduction of miR‐29c‐3p. Subsequently, the low level of miR‐29c‐3p in the CAF‐derived exosomes contributes to the derepression of MMP2, which promotes the aggression of OC cells.

Background Brucellosis is a major public health problem that seriously affects developing countries and could cause significant economic losses to the livestock industry and great harm to human health. Reasonable prediction of the incidence is of great significance in controlling brucellosis and taking preventive measures. Methods Our human brucellosis incidence data were extracted from Shanxi Provincial Center for Disease Control and Prevention. We used seasonal-trend decomposition using Loess (STL) and monthplot to analyse the seasonal characteristics of human brucellosis in Shanxi Province from 2007 to 2017. The autoregressive integrated moving average (ARIMA) model, a combined model of ARIMA and the back propagation neural network (ARIMA-BPNN), and a combined model of ARIMA and the Elman recurrent neural network (ARIMA-ERNN) were established separately to make predictions and identify the best model. Additionally, the mean squared error (MAE), mean absolute error (MSE) and mean absolute percentage error (MAPE) were used to evaluate the performance of the model. Results We observed that the time series of human brucellosis in Shanxi Province increased from 2007 to 2014 but decreased from 2015 to 2017. It had obvious seasonal characteristics, with the peak lasting from March to July every year. The best fitting and prediction effect was the ARIMA-ERNN model. Compared with those of the ARIMA model, the MAE, MSE and MAPE of the ARIMA-ERNN model decreased by 18.65, 31.48 and 64.35%, respectively, in fitting performance; in terms of prediction performance, the MAE, MSE and MAPE decreased by 60.19, 75.30 and 64.35%, respectively. Second, compared with those of ARIMA-BPNN, the MAE, MSE and MAPE of ARIMA-ERNN decreased by 9.60, 15.73 and 11.58%, respectively, in fitting performance; in terms of prediction performance, the MAE, MSE and MAPE decreased by 31.63, 45.79 and 29.59%, respectively. Conclusions The time series of human brucellosis in Shanxi Province from 2007 to 2017 showed obvious seasonal characteristics. The fitting and prediction performances of the ARIMA-ERNN model were better than those of the ARIMA-BPNN and ARIMA models. This will provide some theoretical support for the prediction of infectious diseases and will be beneficial to public health decision making.

Purpose Integrins are critical to cancer progression. Integrin alpha 5 (ITGA5) is correlated with the prognosis of cervical cancer patients. However, whether ITGA5 plays an active role in cervical cancer progression or not remains unknown. Methods ITGA5 protein expression was detected in 155 human cervical cancer tissues by immunohistochemistry. Data from The Cancer Genome Atlas were utilized to identify risk factors for the overall survival of cervical cancer patients and ITGA5‐associated differentially expressed genes. Analyses of single‐cell RNA‐seq based on Gene Expression Omnibus datasets were performed to show the coexpression of ITGA5 and angiogenesis factors. Tube formation assay, 3D spheroid sprout assay, qRT‐PCR, Western Blotting, ELISA, and immunofluorescence were conducted to explore the angiogenic function of ITGA5 in vitro and underlying mechanisms. Results High ITGA5 level was significantly correlated with increased risk in terms of overall survival and advanced disease stage in cervical cancer patients. ITGA5‐associated differentially expressed genes linked ITGA5 to angiogenesis, and immunohistochemistry showed a positive correlation between ITGA5 and microvascular density in cervical cancer tissues. Moreover, tumor cells transfected with ITGA5‐targeting siRNA decreased ability to promote endothelial tube formation in vitro. ITGA5/VEGFA coexpression was observed in a tumor cell subpopulation and the decreased endothelial angiogenesis by downregulating ITGA5 could be reversed by VEGFA. Bioinformatics analysis highlighted the PI3K‐Akt signaling pathway as downstream of ITGA5. Downregulation of ITGA5 in tumor cells significantly decreased p‐AKT and VEGFA levels. Fibronectin (FN1) coated cells or transfected with FN1‐targeting siRNA showed fibronectin may play a critical role on ITGA5‐mediated angiogenesis. Conclusion ITGA5 promotes angiogenesis and possibly be a potential predictive biomarker for poor survival of patients in cervical cancer. ITGA5 promotes angiogenesis in cervical cancer by AKT/VEGFA axis and Fibronectin playscritical role in this pathway.

Albumin (ALB) and blood urea nitrogen (BUN) are both associated with the prognosis of acute ischemic stroke (AIS). A recent prognostic marker, the BUN/ALB ratio (BAR), has been suggested as a simple and sensitive method to predict certain acute diseases. To determine the predictive value of BAR in relation to the risk of in-hospital mortality among AIS patients. Retrospective cohort study. Cox regression analysis was employed to assess the relationship between in-hospital mortality and BAR, with hazard ratios (HRs) and 95% confidence intervals. Subgroup analysis of acute pulmonary embolism, acute myocardial infarction (AMI), thrombolysis, thrombectomy, and septic shock was performed to further examine this relationship. The predictive value of BAR and BAR multivariate models for in-hospital mortality was evaluated and compared to BUN, ALB, the Acute Physiology and Chronic Health Evaluation IV (APACHE IV) score, and the Sequential Organ Failure Assessment Score (SOFA). Among the 1,635 eligible patients, 226 (13.81%) died during hospitalization. An elevated serum BAR level was associated with an increased in-hospital mortality risk (HR: 1.3) after covariates were adjusted. Additionally, this positive association was observed in patients without AP, AMI, thrombolysis, history of thrombectomy, or septic shock (all; < 0.05). The efficacy of the BAR multivariate model in predicting in-hospital mortality among AIS patients was superior to that of both APACHE IV and SOFA, with an area under the curve of 0.87. Serum BAR exhibits the potential to identify AIS patients with high mortality risk, which may contribute to enhanced disease surveillance and risk stratification.

Background Cervical cancer is one of the most common malignancies in women, and its progression is closely associated with hypoxia and tumor-associated macrophages (TAMs) polarization in the tumor microenvironment (TME). The dynamic interaction between cancer cells and TAMs in the hypoxic tumor microenvironment is the key to promoting tumor progression. However, the mechanisms underlying this interaction remain unclear. Methods Macrophages were exposed to hypoxia, and next-generation sequencing was performed to analyze differentially expressed genes. TCGA data were used to identify the up-regulated genes that were independently associated with patient survival. Multiplex immunofluorescence was employed to identify semaphorin 6B (SEMA6B)-positive TAMs in human cervical cancers, and the correlation between their infiltration and clinicopathological characteristics were analyzed. TCGA, GEO, and TIMER databases were used to assess correlations between SEMA6B expression and patient survival and immune cell infiltration patterns. The SEMA6B expression in macrophages was knocked down by using siRNAs. Cell proliferation and mobility were evaluated in vitro, and flow cytometry, PCR and Western Blotting were conducted to determine the polarization of macrophages. Results After exposure to hypoxia, 236 genes were up-regulated in macrophages. Among them, SEMA6B exhibited a significant association with poor survival. In addition, abundant CD206 + SEMA6B + TAMs were associated with poor prognosis in cervical cancer patients. Database analysis revealed that SEMA6B expression was positively correlated with the infiltration of M2 macrophages and Tregs and negatively correlated with the infiltration of CD4 + and CD8 + T cells. In vitro, knocking down SEMA6B in macrophages inhibited macrophage M2 polarization and the migration ability of macrophages. Furthermore, after coculture of macrophages with SEMA6B knockdown and cervical cancer cells, the proliferation, migration and invasion of SiHa and HeLa cells was significantly reduced in vitro. Conclusions Knocking down SEMA6B may attenuate the progression of cervical cancer through regulating macrophage M2 polarization. The mechanism of action of SEMA6B on the progression of cervical cancer in vivo still needs further research. Targeting SEMA6B may be a potential immunotherapy approach for treating cervical cancer.

Background Cancer stem cells (CSCs) are considered the ‘seeds’ of recurrence after chemotherapy, but eliminating CSCs remains notoriously challenging. This study aims to examine whether cell cycle checkpoint kinase 1 (CHK1) blockade can abrogate the stemness of ovarian cancer (OC) cells, making them easier targets of anti-tumor immunity. Methods Prexasertib was used to block CHK1 in OC cell lines and xenografts, and its cytotoxicity was assessed in vitro and in vivo . In vitro tumor-sphere formation assays and stemness markers were used to evaluate cell stemness. PD-L1 expressions were examined via qRT-PCR, Western blot, flow cytometry, and immunohistochemistry. Prexasertib in combination with anti-PD-L1 antibody Atezolizumab was tested in immune-proficient mice bearing OC xenografts in terms of effects on tumor growth, tumor cell stemness, and tumor infiltrating lymphocytes via tumor volume monitoring, immunohistochemistry, and flow cytometry. Results Prexasertib effectively inhibited CHK1 phosphorylation, exhibited significant anti-tumor effects in vitro and in vivo , accompanied by decreased OC cell stemness. CHK1 was highly expressed in tumor spheres versus tumor cells cultured in 2D system, and Prexasertib treatment suppressed sphere formation and reduced the ALDH + cell fraction. Unexpectedly, Prexasertib upregulated PD-L1 expression in tumor cells. In vivo , combining Prexasertib with Atezolizumab led to more remarkable remission of tumors, when compared with Prexasertib or Atezolizumab alone. Meanwhile, the tumor-infiltrating CD8 + T cells significantly increased in the combination group, while exhausted T cells decreased; the treatments did not affect CD4 + cell infiltration. Conclusion Dual targeting of CHK1 and PD-L1 may improve OC treatment by simultaneously suppressing stemness and enhancing anti-tumor immunity.