Early-onset hepatocellular carcinoma (HCC) is insidious, with characteristics of easy metastasis, high recurrence rate, and significant mortality. To address the substantial time and resource demands associated with HCC prognostic prediction, we extract meaningful insights from limited small-sample data to develop and validate a prediction model for HCC 5-year overall survival (OS) by machine learning (ML). 76 newly diagnosed patients with HCC were eventually enrolled between September 2018 and July 2019. The follow-up time was 1–67 months. Patients who survived for 5 years after the first surgery, were divided into a surviving group (n = 34) and a nonsurviving group (n = 42). Pathological data and related survival factors of patients were collected before treatment. The final subset of features was filtered. Prediction models for 5-year OS in patients with HCC were established by logistic regression (LR), support vector machine (SVM), decision tree classification (DTC), random forests (RF), and extreme gradient Boosting (XGBoost), respectively. Additionally, the optimal model was established after rigorous validation. The models were evaluated by values of specificity, F1 score, recall, accuracy and area under the receiver operating characteristic curve (AUC-ROC). The decision curve analysis (DCA) method was assessed the evaluation. Finally, internal and external validations were performed to further validate model’ robustness. The significant variable set, which included 22 variables, was screened. Ranking the importance of variables, the top 22 characteristic variables were as follows: the maximum diameter, presence or absence of distant metastasis, CNLC stage, ALB, age, RBC, the large size CTC, total bilirubin (TBIL), PD-L1 (-) CTC, ≥ Pentaploid CTC, AFP, vascular cancer thrombus and satellite nodules, WBC, CTC, BCLC stage, multiple nodules, AST, PD-L1 (-) CTC-WBC cluster, Triploid CTC, LYM, PD-L1 (-) CEC-WBC cluster and degree of cirrhosis. The AUC-ROC values for predicting the 5-year OS rate of HCC patients by the logistic regression, SVM, DTC, RF, and XGBoost models were 0.737, 0.971, 0.657, 0.741, and 0.703, respectively. Among them, the SVM model had the best performance (Accuracy = 0.987, F1 score = 0.988, Recall value = 1.000). The SVM algorithm demonstrated superior performance and stability in the internal and external validations of the model. The SVM model could predict the 5-year OS in HCC with good recognition ability and achieves significantly greater accuracy compared to traditional models. Diagnosis and treatment could be utilized to intervene in the risk factors in this model, thereby improving patient prognosis.

Background: For detecting genotype-phenotype association from case–control single nucleotide polymorphism (SNP) data, one class of methods relies on testing each genomic variant site individually. However, this approach ignores the tendency for associated variant sites to be spatially clustered instead of uniformly distributed along the genome. Therefore, a more recent class of methods looks for blocks of influential variant sites. Unfortunately, existing such methods either assume prior knowledge of the blocks, or rely on ad hoc moving windows. A principled method is needed to automatically detect genomic variant blocks which are associated with the phenotype.Results: In this paper, we introduce an automatic block-wise Genome-Wide Association Study (GWAS) method based on Hidden Markov model. Using case–control SNP data as input, our method detects the number of blocks associated with the phenotype and the locations of the blocks. Correspondingly, the minor allele of each variate site will be classified as having negative influence, no influence or positive influence on the phenotype. We evaluated our method using both datasets simulated from our model and datasets from a block model different from ours, and compared the performance with other methods. These included both simple methods based on the Fisher’s exact test, applied site-by-site, as well as more complex methods built into the recent Zoom-Focus Algorithm. Across all simulations, our method consistently outperformed the comparisons.Conclusions: With its demonstrated better performance, we expect our algorithm for detecting influential variant sites may help find more accurate signals across a wide range of case–control GWAS.

China’s unique economic growth target system exerts significant economic growth pressure (EGP) on local officials, leading to notable economic and environmental consequences for urban land use. Consequently, this system is theoretically expected to have a significant impact on urban land green utilization efficiency (ULGUE). This study investigates the invisible institutional factors that shape ULGUE within China’s distinct economic growth target system. The results indicate an inverted U-shaped relationship between EGP and ULGUE, and this nonlinear relationship is statistically significant in central, western, and northeastern cities but not in eastern cities. EGP influences ULGUE in a non-monotonic manner by affecting land marketization, green technology innovation, and industrial structure upgrading. Furthermore, environmental regulation and financial technology investment moderate the relationship between EGP and ULGUE. Heterogeneity analysis reveals that the inverted U-shaped relationship is more pronounced in resource-dependent cities and cities with stringent target constraints. This study contributes empirical evidence on the interaction between EGP and ULGUE while offering both theoretical insights and practical implications.

The purpose of the research was to evaluate the safety and effectiveness of the X-ray-free improved simple bedside method for emergency temporary pacemaker implantation as well as the practicability of the method in primary hospitals. Patients [including those suffering from sick sinus syndrome and third-degree and advanced atrioventricular blockage (AVB)] who needed emergency temporary pacemaker implantation from July 2017 to August 2020 in Hunan Provincial People’s Hospital were selected. They were stochastically divided into a research group (95 cases) treated with the improved simple bedside method and a control group (95 cases) with X-ray guidance. The ordinary bipolar electrodes were used in both groups. On this condition, the operation duration, the first-attempt success rate of electrodes, pacing threshold, success rate of the operation, the rate of electrode displacement, and complications in the two groups were separately calculated. The comparison results of the research group with the control group are shown as follows: operation time [(18 ± 5.91) min vs. (43 ± 2.99) min, P < 0.05], the first-attempt success rate of the electrode (97% vs. 98%, P > 0.05), pacing threshold [(0.97 ± 0.35) vs. (0.97 ± 0.32) V, P > 0.05], success rate of the operation (98.9% vs. 100%, P > 0.05), the rate of electrode displacement (8.4% vs. 7.3%, P > 0.05) and complications (3.2% vs. 2.1%, P > 0.05). The emergency temporary pacemaker implantation based on the improved simple bedside method is as safe and effective as the surgical method under X-ray guidance, and the operation is simpler and easier to learn and requires a shorter operating time, therefore, it is more suitable for use in emergency and primary hospitals.

Amid the ongoing transformation of global climate governance, climate policy uncertainty has emerged as an increasingly important factor influencing both energy and agricultural commodity markets, with direct implications for energy and food security. Using monthly data from 2008 to 2025, this study applies the TVP-VAR-DY and TVP-VAR-BK frameworks, together with complex network analysis, to investigate spillover dynamics among climate policy uncertainty, energy, and agricultural markets from both time-varying and frequency-based perspectives. The results show that spillover effects evolve substantially over time and become more pronounced during periods of major external shocks, particularly under the influence of short-run factors. Notably, the transmission effect of climate policy uncertainty is stronger for bioenergy-related agricultural commodities, especially soybeans and corn. While the agricultural market exhibits strong internal connectedness, cross-market risk transmission is heterogeneous across commodities, with corn remaining a relatively stable net transmitter of risk. By contrast, crude oil generally acts as a net receiver, whereas climate policy uncertainty behaves as a net receiver in the short run but gradually shifts into a net transmitter over the medium and long term, suggesting a lagged transmission pattern. Robustness checks based on alternative lag lengths, forecast horizons, and CPU proxies confirm that the main connectedness structure is stable and not driven by specific parameter choices. These findings provide useful evidence for policymakers seeking to improve the stability and transparency of climate policy and mitigate cross-market risk, while also offering practical guidance for investors in portfolio allocation and hedging against policy-induced volatility.

Motivation: Inferring gene networks provides insights into biological pathways and functional relationships among genes. When gene expression samples exhibit heterogeneity, they may originate from unknown subtypes, prompting the utilization of mixture Gaussian graphical model (GGM) for simultaneous subclassification and gene network inference. However, this method overlooks the heterogeneity of network relationships across subtypes and does not sufficiently emphasize shared relationships. Additionally, GGM assumes data follows a multivariate Gaussian distribution, which is often not the case with zero-inflated scRNA-seq data. Results: We propose an Adaptive Joint Graphical Model (AJGM) for estimating multiple gene networks from single-cell or bulk data with unknown heterogeneity. In AJGM, an overall network is introduced to capture relationships shared by all samples. The model establishes connections between the subtype networks and the overall network through adaptive weights, enabling it to focus more effectively on gene relationships shared across all networks, thereby enhancing the accuracy of network estimation. On synthetic data, the proposed approach outperforms existing methods in terms of sample classification and network inference, particularly excelling in the identification of shared relationships. Applying this method to gene expression data from triple-negative breast cancer confirms known gene pathways and hub genes, while also revealing novel biological insights. Availability and implementation: The Python code and demonstrations of the proposed approaches are available at https://github.com/yyytim/AJGM, and the software is archived in Zenodo with DOI: 10.5281/zenodo.14740972.