Fosun Pharma (China)

Vessels encapsulating tumor clusters (VETC) serve as a crucial adverse prognostic indicator in hepatocellular carcinoma (HCC). This study aimed to develop and validate a Delta radiomics-based nomogram model on dynamic contrast-enhanced CT (DCE-CT) to predict VETC status and patient prognosis in HCC. A cohort of 222 patients from two centers with HCC undergoing DCE-CT scans and CD34 immunochemical staining was enrolled. Each liver lesion was segmented on intratumoral and peritumoral regions in the arterial phase (AP) and portal vein phase (PP) CT images. A total of 10,128 (1,688*6) radiomics features, including absolute and relative delta radiomics features, were extracted. Using four machine-learning algorithms, the features were trained and optimized (training set), and validated (internal and external test sets) to classify VETC patterns. Multivariable logistic regression incorporating signature scores and clinical predictors generated the nomogram. Model performance was evaluated through area under the curves (AUC) analysis, calibration curves, and decision curve analysis (DCA). The Kaplan-Meier survival analysis was used to assess recurrence-free survival (RFS) in the VETC+ and VETC- patients. The logistic regression-based nomogram incorporating three radiomic signatures and two clinical factors showed powerful predictive ability in internal and external test sets with AUCs of 0.854 and 0.803, respectively. The calibration curves, DCA showed favorable predictive performance of the nomogram. Patients classified as high-risk by the nomogram exhibited significantly shorter RFS compared to low-risk counterparts (P < 0.001). The developed nomogram demonstrated clinical translatability in preoperative VETC prediction and recurrence risk stratification, providing a potential imaging biomarker for guiding personalized therapeutic strategies in HCC management.

With the increasing focus on sustainable development in the world, green sports policies have become a key factor in promoting environmental management and circular economies in high-impact events. This article critically assesses the role of this type of policies in the performance of waste management of the Chinese sports activities during 2015-2024 based on the extensive data sample of 1,600 observations of different event types (e. g., Olympics, National Games) and cities. This study utilizes a multi-method framework, such as descriptive statistics, Pearson, OLS regression, K-Means clustering and Difference-in-Differences (DiD) modeling, to identify direct policy impacts and contextual influences, such as infrastructure and temporality. The key distinguishing feature of the work in the novel delivery is the massive econometric integration with spatial-temporal analysis, which is the first quantitative characterization of the policy-outcome relationships in the Chinese sports green sector. In contrast to previous qualitative or single cases studies, it measures non-linear relationships by using clustering to measure performance typologies and variability metrics to measure convergence trends, providing replicable information on implementation variances. Findings indicate a strong baseline recycling level of about 84, which underlines the sustainability status quo, but OLS models have low explanatory potential (R2=0.008), indicating complex interplay between infrastructure and behaviour. The estimates of DiD confirm a small effect of 3-5% increase in recycling after the implementation of policies in 2018 with the elitist cities (Shanghai, Beijing, Chengdu) outperforming the mid-tier areas through hightech logistics. In spite of transitional frictions, waste diversion has been institutionalized in policies but the gains depend on local innovations. Such findings will provide policy-makers with evidence-based solutions like digital supervision and scalable event-plans to achieve greater efficiency, which meets the national ecological civilization and Dual Carbon goals of efficient green sports governance.

DaxibotulinumtoxinA for injection (DAXI), the first long-acting botulinum toxin (BoNT) type A, is FDA approved for cervical dystonia (CD). DAXI's novel formulation, which includes a custom-engineered peptide, is designed to provide an extended duration of clinical benefit. To evaluate the pooled efficacy and safety of DAXI for CD across two phase 3, multicenter, randomized, double-blind, placebo-controlled trials: ASPEN-1, conducted in North America and Europe, and ASPEN-1-CN, a similarly designed, smaller pivotal clinical trial, conducted in China. Adults with moderate-to-severe CD were randomized (3:3:1) to receive DAXI 125U, DAXI 250U, or placebo. The primary endpoint was change from baseline in Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS) total score averaged across weeks 4 and 6. A key secondary endpoint was duration, defined as time until loss of >80% of peak effect. In all, 357 subjects were randomized and received DAXI 125U (n=149), DAXI 250U (n=154), or placebo (n=54). DAXI 125U (-12.0) and DAXI 250U (-11.9) significantly improved the mean TWSTRS total score versus placebo (-4.6; P<0.0001). Median (95% CI) duration of effect was 24.1 (20.6-28.9) weeks for DAXI 125U and 22.0 (20.1-24.3) weeks for DAXI 250U. Rates of treatment-related dysphagia (125U: 4.7%, 250U: 4.5%) and muscle weakness (125U: 5.4%, 250U: 4.5%) were low for both active doses. This pooled analysis of two phase 3 trials demonstrates that DAXI is an effective, safe, and long-acting treatment for CD. Key adverse events occurred at rates lower than prior pivotal trials of BoNTs for CD.

The gut microbiome plays a significant role in the development of type 2 diabetes (T2D), yet its underlying mechanisms remain unclear. Explainable artificial intelligence offers a promising approach to elucidating machine learning models and enhancing our understanding of these mechanisms. However, identifying the most effective explainer for specific problems remains a significant challenge. To address this issue, we propose the consistency- and informativeness-aware explanation framework (CIAE), which integrates multiple explainers to improve the consistency and informativeness of explanations. Using the top 100 Kyoto Encyclopedia of Genes and Genomes (KEGG) Orthology (KO) features identified by CIAE, models achieved robust results on individual datasets (area under the curve (AUC): 0.870 and 0.808) and demonstrated strong cross-dataset generalization (AUC: 0.822 and 0.778). Notably, K00688 and K25026 were selected as key KOs in both datasets, and they are involved in the regulation of the starch and sucrose metabolism pathway and blood glucose homeostasis, which are closely related to the occurrence of diabetes.

Type 2 diabetes mellitus (T2DM), a complex systemic metabolic disorder caused by multiple factors, has been linked to numerous acute and chronic complications. T2DM pathogenesis includes glucotoxicity, lipotoxicity, inflammatory cytokines, and amyloid formation. Within the pancreatic islet microenvironment, the crosstalk among cell types plays a significant role in these pathogenic mechanisms. Islet β cells, macrophages, and endothelial cells, the three primary cell types, engage in intercellular communication under physiological and pathological conditions, critical to maintaining islet homeostasis and promoting the pathological progression of T2DM. This review discusses the interactions between these islet cells, particularly how their crosstalk affects islet function and T2DM development. Additionally, natural products targeting islet cell interactions are discussed as a therapeutic approach for T2DM, along with other personalized treatment options, including exosomes, parasitic therapy, and dietary interventions. Emerging strategies that regulate intercellular signaling and complex crosstalk within the islet microenvironment offer promising avenues for T2DM treatment.