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The incorporation of heterogeneous nodes can significantly enhance the catalytic and optoelectronic properties of metal-organic networks (MONs) because of the resulting uneven charge distribution and unique metal centers. On-surface synthesis (OSS) has emerged as a powerful strategy for constructing advanced MONs with heterogeneous metal-organic nodes. Herein, we report the fabrication of an MON featuring N-Ag-C heterogeneous nodes, achieved via thermally induced sequential N-OH and C-H activation on Ag(100). During annealing, dehydroxylation proceeds gradually, leading to the formation of metal-organic dimers, tetramers, and extended chains. Notably, thermal treatment at 530 K triggers C-H activation of the precursor's central benzene ring, yielding an MON containing a rarely observed heterogeneous node. The structural evolution of these species is tracked by scanning tunneling microscopy (STM), complemented by high-resolution synchrotron radiation photoemission spectroscopy (SRPES). This study provides fundamental insights into the design and synthesis of heterogeneous MONs with heterogeneous nodes, paving the way for their potential applications in catalysis and optoelectronics.
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http://dx.doi.org/10.1021/acs.jpclett.5c01145 | DOI Listing |
Front Endocrinol (Lausanne)
September 2025
Department of Medical Ultrasound, Affiliated Hospital of Jiangsu University, Zhenjiang, China.
Background: Given the challenge in preoperative diagnosis of high-volume lymph node metastasis (HVLNM) in clinical practice, we constructed and externally validated a comprehensive predictive model that integrated conventional ultrasound characteristics, contrast-enhanced ultrasound (CEUS) parameters, BRAFmutation, and clinicopathological data for HVLNM in clinically lymph node-negative (cN0) papillary thyroid carcinoma (PTC).
Methods: Totally, 126 clinically lymph node-negative (cN0) PTC patients who underwent subtotal or total thyroidectomy and accompanied with prophylactic cervical lymph node dissection between December 2022 and December 2024 were enrolled in this retrospective study, and an additional 47 cN0 PTC patients included for the external validation cohort. Univariate and multivariate analysis were performed to identify the independent risk factors for HVLNM, and a binary logistic regression equation and relevant nomogram was constructed to predict the risk about HVLNM.
J Imaging Inform Med
September 2025
Heart Center, Department of Geriatrics, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, 310014, Zhejiang, China.
The growing heterogeneity of cardiac patient data from hospitals and wearables necessitates predictive models that are tailored, comprehensible, and safeguard privacy. This study introduces PerFed-Cardio, a lightweight and interpretable semi-federated learning (Semi-FL) system for real-time cardiovascular risk stratification utilizing multimodal data, including cardiac imaging, physiological signals, and electronic health records (EHR). In contrast to conventional federated learning, where all clients engage uniformly, our methodology employs a personalized Semi-FL approach that enables high-capacity nodes (e.
View Article and Find Full Text PDFJ Pharmacol Toxicol Methods
September 2025
Department of Pharmacology, Faculty of Pharmacy, Kabul University, 1006 Kabul, Afghanistan.
Polypharmacy during tuberculosis (TB) treatment, particularly in patients with comorbidities such as diabetes mellitus (DM), significantly increases the risk of adverse drug reactions (ADRs) due to complex drug-drug interactions (DDIs). Existing computational methods primarily focus on pairwise drug interactions, often failing to capture the multifactorial nature of ADRs in polypharmacy contexts. To address this gap, we developed PolyCheck, a hybrid predictive model that integrates network-based and rule-based methods to identify potential ADRs arising from multi-drug regimens.
View Article and Find Full Text PDFNucl Med Commun
September 2025
Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland.
Background: Lymphoma staging plays a pivotal role in treatment planning and prognosis. Yet, it still relies on manual interpretation of PET/computed tomography (CT) images, which is time-consuming, subjective, and prone to variability. This study introduces a novel radiomics-based machine learning model for automated lymphoma staging to improve diagnostic accuracy and streamline clinical workflow.
View Article and Find Full Text PDFThe purpose of this study is to review the literature and compare the outcomes of lymph node dissection (LND), or lymphadenectomy, versus no lymphadenectomy (no LND) and extended lymphadenectomy (ELND) versus standard lymphadenectomy (SLND) in various commonly diagnosed solid malignancies with high mortality rates in the United States. We searched for randomized controlled trials involving high-mortality solid tumors, including prostate, bladder, lung, breast, colorectal, pancreas, liver, endometrial, ovarian, and esophageal cancers, in Medline, Embase, and Cochrane Library. The primary endpoint was overall survival, and secondary endpoints included progression-free survival and complications.
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