Integrated sensing and communication (ISAC) has been envisioned as a promising technique to alleviate the spectrum congestion problem. Inspired by the applications of reconfigurable intelligent surface (RIS) in dynamically manipulating wireless propagation environment, in this paper, we investigate to deploy a RIS in an ISAC system to pursue performance improvement. Particularly, we consider a RIS-assisted ISAC system where a multi-antenna base station (BS) performs multi-target detection and multi-user communication with the assistance of a RIS. Our goal is maximizing the weighted summation of target detection signal-to-noise ratios (SNRs) by jointly optimizing the transmit beamforming and the RIS reflection coefficients, while satisfying the communication quality-of-service (QoS) requirement, the total transmit power budget, and the restriction of RIS phase-shift. An efficient alternating optimization algorithm combining the majorization-minimization (MM), penalty-based, and manifold optimization methods is developed to solve the resulting complicated non-convex optimization problem. Simulation results illustrate the advantages of deploying RIS in ISAC systems and the effectiveness of our proposed algorithm.
Synthetic Aperture Radar (SAR) utilizes the movement of the radar antenna over a specific area of interest to achieve higher spatial resolution imaging. In this paper, we aim to investigate the realization of SAR imaging for a stationary radar system with the assistance of active reconfigurable intelligent surface (ARIS) mounted on an unmanned aerial vehicle (UAV). As the UAV moves along the stationary trajectory, the ARIS can not only build a high-quality virtual line-of-sight (LoS) propagation path, but its mobility can also effectively create a much larger virtual aperture, which can be utilized to realize a SAR system. In this paper, we first present a range-Doppler (RD) imaging algorithm to obtain imaging results for the proposed ARIS-empowered SAR system. Then, to further improve the SAR imaging performance, we attempt to optimize the reflection coefficients of ARIS to maximize the signal-to-noise ratio (SNR) at the stationary radar receiver under the constraints of ARIS maximum power and amplification factor. An effective algorithm based on fractional programming (FP) and majorization minimization (MM) methods is developed to solve the resulting non-convex problem. Simulation results validate the effectiveness of ARIS-assisted SAR imaging and our proposed RD imaging and ARIS optimization algorithms.
This study is focused on ultrasound multimodality examination, which refers to the combined use of three ultrasound examination modalities, ultrasound (US), acoustic radiation force impulse (ARFI) imaging, and contrast-enhanced ultrasound (CEUS). The purpose of this study is to analyze the value of applying ultrasound multimodality examination in the differential diagnosis of benign and malignant breast non-mass-like lesions (NMLs).
This paper proposes a line vulnerability evaluation system based on three indexes, taking into account the factors of power system structure and state. Based on the subjective-objective weighting method, the scores of comprehensive vulnerability are calculated, and finally the list of the top 10 most vulnerable lines is sorted. The effectiveness of the evaluation system is verified by IEEE39 node network.
In recent years, immunotherapy has made significant progress in cancer treatment, especially in malignant tumors that are difficult to cure with traditional therapies, showing unprecedented potential 1 . However, many patients do not exhibit durable responses to immunotherapy in clinical practice, and the issue of drug resistance remains prominent, making immune cell exhaustion one of the main barriers to current cancer immunotherapy 2 . Immune cell exhaustion is a complex process involving prolonged antigen stimulation, activation of immunosuppressive signals, and cellular metabolic dysregulation, ultimately leading to the dysfunction of effector T cells and other immune cells, thereby weakening the anti-tumor immune response
Objective: The purpose of this retrospective study is to analyze the ultrasound characteristics of solitary papillary thyroid carcinoma (PTC) located in the thyroid isthmus, the risk factors for lymph node (LN) metastasis and capsular invasion. Methods: We included a total of 135 patients of solitary PTC located in the thyroid isthmus. All the patients underwent Ultrasound exam, and the routine total thyroidectomy, and prophylactic central LN dissection. Patient's demographics as well as thyroid isthmus nodule's ultrasound characteristics, risk factors associated with LN metastasis and capsular invasion were analyzed. Results: Based on the postoperative clinicopathological analysis, the occurrence of LN metastasis was higher in male than in female (p < 0.001). As risk factors, the size of isthmus PTC associated to LN metastasis and the capsular invasion were p=0.005 and p=0.000, respectively. The area under the ROC curve (AUC) of the size of isthmus PTC was 0.64 (95% CI: 0.55, 0.72), indicating probability for LN metastasis. The capsular invasion was 0.77 (95% CI: 0.68, 0.83). When the threshold was set at 1.1 cm, the larger size was indicated that there was probably an occurrence of LN metastasis with the sensitivity and specificity of 47.4% and 73.7%, respectively. When the threshold was set 0.7 cm, the larger size indicated that there was potentially a capsular invasion, with the sensitivity and specificity of 80.6% and 56.3%, respectively. Wider-than-tall nodules were found to be significantly different from those in LN metastasis and capsular invasion (p=0.038 and p=0.030, respectively). There were significant differences in capsular invasion in extra-thyroidal extension (ETE) compared with smooth or ill-defined and lobulated or irregular nodule (p=0.017). Conclusions: This study showed that the incidence of LN metastasis in male was higher than that in female. When the ultrasound image shows a thyroid isthmus nodule with a wider-than-tall shape, LN metastasis and capsular invasion were likely to occur. When the ultrasound image shows a thyroid isthmus nodule with an ETE, capsular invasion was likely to occur. ETE and wider-than-tall may be an indicator of FNA under ultrasound guidance, even though the size of thyroid isthmus nodule may be less than 1 cm.
Background Colorectal cancer (CRC) poses a global health threat, with the oral microbiome increasingly implicated in its pathogenesis. This study leverages Mendelian Randomization (MR) to explore causal links between oral microbiota and CRC using data from the China National GeneBank and Biobank Japan. By integrating multi-omics approaches, we aim to uncover mechanisms by which the microbiome influences cellular metabolism and cancer development. Methods We analyzed microbiome profiles from 2017 tongue and 1915 saliva samples, and GWAS data for 6692 CRC cases and 27178 controls. Significant bacterial taxa were identified via MR analysis. Single-cell RNA sequencing and enrichment analyses elucidated underlying pathways, and drug predictions identified potential therapeutics. Results MR identified 19 bacterial taxa significantly associated with CRC. Protective effects were observed in taxa like RUG343 and Streptococcus_umgs_2425, while HOT-345_umgs_976 and W5053_sp000467935_mgs_712 increased CRC risk. Single-cell RNA sequencing revealed key pathways, including JAK-STAT signaling and tyrosine metabolism. Drug prediction highlighted potential therapeutics like Menadione Sodium Bisulfite and Raloxifene. Conclusion This study establishes the critical role of the oral microbiome in colorectal cancer development, identifying specific microbial taxa linked to CRC risk. Single-cell RNA sequencing and drug prediction analyses further elucidate key pathways and potential therapeutics, providing novel insights and personalized treatment strategies for CRC.
Abstract G‐protein‐coupled receptors (GPRs) are critical regulators of various biological behaviors, and their role in gastric cancer (GC) progression is gaining increasing attention. Among them, the immune regulatory mechanisms mediated by chemokine receptor 4 (CXCR4) remain insufficiently understood. This study aims to explore the immune regulatory functions of CXCR4 and the heterogeneity of the tumor microenvironment (TME) by examining GPR‐related gene expression in GC. Through multi‐omics approaches, including spatial transcriptomics and single‐cell RNA sequencing, we investigated the oncogenic mechanisms of CXCR4, particularly its role in T cell immune exhaustion. In vitro experiments, including ELISA, PCR, CCK8 assays, cell scratch assays, and colony formation assays, were used to validate the role of CXCR4 in the migration and invasion of AGS and SNU‐1 cell lines. CXCR4 silencing using siRNA further demonstrated its regulatory effects on these cellular processes. Our results revealed a strong correlation between elevated CXCR4 expression and increased exhaustion of regulatory T cells (Tregs) in the TME. Furthermore, heightened CXCR4 expression was linked to increased TME heterogeneity, driven by oxidative stress and activation of the NF‐κB pathway, promoting immune evasion and tumor progression. Silencing CXCR4 significantly inhibited the invasive and proliferative abilities of AGS and SNU‐1 cells, while also reducing the expression of pro‐inflammatory cytokines IL‐1β and interleukin‐6, thus alleviating chronic inflammation and improving TME conditions. In conclusion, our comprehensive investigation highlights CXCR4 as a key mediator of TME dynamics and immune modulation in GC. Targeting CXCR4 presents a promising therapeutic strategy to slow tumor progression by reducing Tregs‐mediated immune exhaustion and TME heterogeneity, positioning it as a novel therapeutic target in GC treatment.
Abstract Objective This study aimed to evaluate the predictive value of implementing machine learning models based on ultrasound radiomics and clinicopathological features in the survival analysis of triple-negative breast cancer (TNBC) patients. Methods and materials All patients, including retrospective cohort (training cohort, n = 306; internal validation cohort, n = 77) and prospective external validation cohort ( n = 82), were diagnosed as locoregional TNBC and underwent pre-intervention sonographic evaluation in this multi-center study. A thorough chart review was conducted for each patient to collect clinicopathological and sonographic features, and ultrasound radiomics features were obtained by PyRadiomics. Deep learning algorithms were utilized to delineate ROIs on ultrasound images. Radiomics analysis pipeline modules were developed for analyzing features. Radiomic scores, clinical scores, and combined nomograms were analyzed to predict 2-year, 3-year, and 5-year overall survival (OS) and disease-free survival (DFS). Receiver operating characteristic (ROC) curves, calibration curves, and decision curves were used to evaluate the prediction performance. Findings Both clinical and radiomic scores showed good performance for overall survival and disease-free survival prediction in internal (median AUC of 0.82 and 0.72 respectively) and external validation (median AUC of 0.70 and 0.74 respectively). The combined nomograms had AUCs of 0.80–0.93 and 0.73–0.89 in the internal and external validation, which had best predictive performance in all tasks ( p < 0.05), especially for 5-year OS ( p < 0.01). For the overall evaluation of six tasks, combined models obtained better performance than clinical and radiomic scores [AUCs of 0.83 (0.73,0.93), 0.81 (0.72,0.93), and 0.70 (0.61,0.85) respectively]. Interpretation The combined nomograms based on pre-intervention ultrasound radiomics and clinicopathological features demonstrated exemplary performance in survival analysis. The new models may allow us to non-invasively classify TNBC patients with various disease outcome.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)