Introduction:Erectile dysfunction (ED) represents a significant global public health challenge in men's health, with its prevalence exacerbated by population aging. Given the rapid advancement of artificial intelligence (AI) in healthcare, there is growing interest in its novel applications for ED diagnosis and treatment. Objectives:This systematic review synthesizes existing research on AI's role in ED management, examining its current development, application in diagnosis and treatment, and key benefits and challenges. Methods:We executed a PRISMA-guided systematic review across PubMed, Web of Science, and Chinese databases (CNKI/Wanfang) through June 19, 2025. Analysis targeted three domains: (1) the technological evolution of AI tools specific to ED, (2) applications in clinical diagnosis, prediction, and personalized treatment, and (3) the emerging role of large language models (LLMs). Results:The review identified major milestones in AI's technological evolution for ED and highlighted its significant clinical advantages, particularly through intelligent questionnaires and wearable devices enabling precise diagnosis, alongside efficacy in developing personalized treatments and predicting disease progression. While AI, particularly LLMs, demonstrates emerging potential, critical challenges persist. Conclusion:This review establishes a critical theoretical and technical foundation for implementing AI in men's healthcare, demonstrating its significant potential to transform the management of ED through improved diagnostics, personalized treatment, and predictive capabilities. While the interpretive nature of the synthesis presents an inherent limitation, cross-validation among researchers enhanced the reliability of the findings. However, clinical adoption remains contingent upon addressing key challenges related to data privacy, ethical considerations, and interoperability.

Overactive bladder (OAB) is a common urological disorder with an incompletely understood pathogenesis that markedly impacts patients’ quality of life, and hepatitis C virus (HCV) infection is associated with systemic inflammation and extrahepatic complications, with their potential association remaining understudied. This study thus aims to investigate the association between HCV and OAB in adults. This study aims to investigate the association between hepatitis C virus (HCV) and overactive bladder (OAB) in adults. This study analyzed data from the National Health and Nutrition Examination Survey (NHANES) conducted between 2013 and 2018. Logistic regression analysis, subgroup analysis, and interaction tests were employed to assess the association between HCV and OAB. Additionally, propensity score matching (PSM), inverse probability weighting (IPTW), and overlap weighting (OW) were employed to control for confounding factors. E-value analysis was conducted to assess the robustness of results against unmeasured confounders, while sensitivity and specificity analyses evaluated the predictive performance of HCV for OAB. This study included 14,012 patients aged ≥ 20 years. Logistic regression analysis demonstrated an association between HCV and OAB (OR = 2.06, 95

e16522 Background: WHO/ISUP grade is a significant risk factor for the prognosis of patients with clear cell renal cell carcinoma (ccRCC) and effects the response to tyrosine kinase inhibitors (TKIs) for advanced-stage patients. The purpose of this study was to develop a fully-automated model that can predict WHO/ISUP grade based on three-phase CT images, and may implicate the TKIs response. Methods: A total of 373 patients with ccRCC from three medical centers were retrospectively included in the study, with 261 in the training set and 112 in the testing set. CT images of 166 TCGA-KIRC cohort were used to explore the different expressed genes and enriched biological pathways related to the radiomics model. All CT phases were aligned to the venous phase and used to evaluate a presenting deep learning model (Kidney and kidney tumor segmentation 2023, KiTS23) for kidney tumor segmentation. Radiomics features were extracted from the tumor of original CT phases. Linear discriminant analysis was used to develop three models based on transcriptomic features, radiomics features, and both features combined. Models were evaluated by area under curve, sensitivity, and specificity. Results: The average dice coefficients of kidney tumor segmentation were 0.87 in the training set and 0.83 in the testing set. For WHO/ISUP grade prediction, in the testing set, the model based on radiomics (AUC = 0.801) outperformed the model based on transcriptomic features (AUC = 0.783). The hybrid model based on transcriptome and radiomics features achieved the best performance in both the training set (AUC = 0.911) and testing set (AUC = 0.859). Moreover, the hybrid model also provided the highest accuracy (0.930), sensitivity (0.714), specificity (0.972), positive predictive value (0.833), and negative predictive value (0.946). The TCGA-KIRC cohort were divided into high- and low-risk group based on the radiomics model prediction, and the differential expressed gene in high-risk group were significantly enriched on the pathway of EGFR tyrosine kinase inhibitor resistance. Conclusions: The fully-automated model based on transcriptome and radiomics features can accurately predict the WHO/ISUP grade of patients with ccRCC, and implicate the TKIs response for advanced-stage patients.

IntroductionRenal cell carcinomas (RCC) are resistant to chemotherapy and radiotherapy, and effective treatment options remain limited. Immunotherapy has emerged as a promising approach, and circular RNAs (circRNAs) are increasingly recognized as key regulators of tumor immunity. This study aims to elucidate the regulatory relationship between circPVT1 and antitumor immunosuppression in RCC.MethodsCircPVT1 expression and its prognostic value were analyzed in RCC samples. The effects of circPVT1 knockdown on RCC cell proliferation, invasion, and metastasis were examined in vitro. Singlecell sequencing was used to assess macrophage infiltration in circPVT1high versus circPVT1low groups. ELISA was performed to measure secretion of IL4, IL10, and TGFβ by tumor cells. In addition, nanotherapeutic system delivering circPVT1 inhibitors was tested for its antitumor efficacy.ResultsCircPVT1 was highly expressed in RCC and correlated with poor prognosis. Knockdown of circPVT1 significantly suppressed cell proliferation, invasion, and metastasis. Singlecell sequencing revealed increased macrophage infiltration in the circPVT1high group. ELISA results demonstrated that circPVT1 knockdown reduced tumor cell secretion of IL4, IL10, and TGFβ, thereby promoting macrophage polarization toward the M1 phenotype. Mechanistically, circPVT1 promoted RCC progression by regulating EMT. Furthermore, a nanotherapeutic system containing circPVT1 inhibitors effectively inhibited RCC growth.DiscussionThese findings indicate that circPVT1 plays a critical role in RCC immune evasion by modulating macrophage polarization and EMT. Therefore, circPVT1 may be a predictor of ccRCC immune evasion and a potential therapeutic target.

Background:Dietary factors, particularly meat and fish intake, may influence kidney stone formation, but evidence in the Chinese population is limited. Objective:To investigate the association between various types of meat and fish consumption and kidney stone prevalence using a food frequency questionnaire (FFQ). Methods:A cross-sectional study was conducted with 830 participants (299 kidney stone patients, 531 controls) using online and hospital-based FFQs. Logistic regression models assessed associations, adjusting for confounders. Results:Pork intake showed a non-linear association with kidney stone status. Compared with the lowest intake group, the highest pork intake group was associated with higher odds of kidney stones (OR = 3.321, 95% CI: 1.094-10.044), whereas the highest sea fish intake group was associated with lower odds (OR = 0.331, 95% CI: 0.115-0.936). Processed meat intake was not significantly associated with kidney stone status after adjustment. Conclusion:Pork consumption is positively associated with kidney stone, and sea fish intake is inversely associated with kidney stone in the Chinese population.

BACKGROUND:Kidney stones are a globally prevalent condition, but their pathogenesis remains incompletely understood. This study aimed to identify and validate key genes implicated in kidney stone formation through sequencing data analysis, offering novel molecular targets for elucidating the underlying pathogenic mechanisms. METHODS:A mouse model of kidney stones was established, and perinephrolithic renal tissue samples were obtained from patients who underwent nephrectomy due to renal dysfunction caused by kidney stones. Transcriptome sequencing was employed to identify differentially expressed genes (DEGs) common to both human and mouse samples. Expression of DEGs in kidney stone specimens was validated using real-time quantitative polymerase chain reaction (qRT-PCR), western blotting, and immunofluorescence (IF). Concurrently, Gene Ontology (GO) and Kyoto Kncyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to investigate functional and pathway associations of the DEGs. RESULTS:Transcriptome sequencing identified eight genes consistently upregulated in both humans and mice: COL3A1, TYROBP, MMP2, BASP1, COL5A1, ITGAX, C1QC, and S100A8. qRT-PCR results demonstrated elevated mRNA levels of C1QC and COL3A1 in the perinephrolithic renal tissues of the mouse model. Western blotting confirmed upregulation of COL3A1 at the protein level, and IF staining further verified the specific enrichment of COL3A1 in the renal tissues of the mouse kidney stone model. GO and KEGG enrichment analyses revealed significant associations between the DEGs and immune-related signaling pathways, as well as biological processes involved in extracellular matrix (ECM) remodeling. CONCLUSIONS:These findings suggest that the ongoing upregulation of COL3A1 could be a key factor in the pathogenesis of kidney stone. Functional enrichment analyses indicate that COL3A1 may promote stone deposition and progression by regulating ECM remodeling and the immune response . These results provide new insights into the molecular mechanisms underlying kidney stone formation.

Loss of major histocompatibility complex (MHC)-I is a hallmark of prostate cancer (PCa) immune evasion and immunotherapy failure. Here, we identify ZNF263 as a transcriptional repressor that silences MHC-I by recruiting nucleosome-remodeling and deacetylase (NuRD) to the STAT1 promoter, reducing STAT1 and MHC-I expression. Hypoxia enhances this repression through two ZNF263 modifications: phosphorylation-driven phase separation that strengthens NuRD interaction and O-GlcNAcylation at S662 that aids STAT1 promoter binding. O-GlcNAcylation also promotes interaction with protein kinase, DNA‑activated catalytic subunit (PRKDC), amplifying phosphorylation. Interferon‑gamma (IFN‑γ)‑induced MHC-I induction is augmented upon ZNF263 loss. In silico docking identified Viroptic as a Krüppel‑associated box (KRAB) pocket binder disrupting ZNF263-NuRD, derepressing STAT1, and potentiating IFN-γ antitumor immunity in vivo. High ZNF263 correlates with low MHC-I, scarce CD8+ T cells, and poor survival, providing rationale for targeting ZNF263 in PCa immunotherapy.

BACKGROUND:To compare the peri-operative outcomes between the minimally-invasive surgery (MIS) and open surgery for the resection of perirenal tumors larger than 7 cm. METHODS:This retrospective single-center study included 46 patients who underwent resection of retroperitoneal perirenal tumors larger than 7 cm between April 2009 and March 2025. Patients were stratified into MIS (laparoscopic or robot-assisted; n = 26) and open surgery (n = 20) groups. Demographic, peri-operative, and pathological variables were collected and analyzed. Subgroup analyses were further performed in patients with tumors larger than 10 cm. Univariate and multivariate logistic regression analyses were conducted to identify independent risk factors for peri-operative blood transfusion, complications, and postoperative hospital stay. RESULTS:Baseline characteristics were comparable except for tumor size, which was significantly larger in the open surgery group (13.36 ± 5.49 vs. 9.43 ± 2.31 cm, P = 0.006). MIS was associated with lower estimated blood loss (90.00 vs. 350.00 mL, P < 0.001), and shorter postoperative hospital stays (9.00 ± 3.83 vs.5.23 ± 1.31 days, P < 0.001). Similar results were observed in the subgroup of patients with tumors larger than 10 cm. Adjacent organ resection was more frequent in the open group (65.00% vs. 34.60%, P = 0.041). Overall complication rate was similar (65.00% vs. 46.20%, P = 0.203), but all three major complications (Clavien-Dindo grade ≥ III) occurred in the open group. On multivariate analysis, surgical method (open or MIS) was identified as an independent predictor of postoperative hospital stay (OR 0.074, P < 0.001), while adjacent organ resection was the only independent risk factor for peri-operative complications (OR 8.481, P = 0.005). CONCLUSION:Both MIS and open surgery are safe and effective selection for patients with perirenal tumors larger than 7 cm. MIS may offer faster recovery and less blood loss, while open surgery remains necessary for large tumors. Multicenter prospective studies with extended follow-up are needed to validate these findings.

Tubulointerstitial fibrosis is a critical and irreversible process of chronic kidney disease. Dedifferentiated proximal tubular cells (PTCs) after injury are important for tubulointerstitial fibrosis. Hepatocyte nuclear factor 4 alpha (HNF4A) is the main regulatory factor for PTC differentiation. However, its role in PTC dedifferentiation and kidney fibrosis remains unclear. To investigate the role of HNF4A in kidney fibrosis, bioinformatics analysis and in vivo models were used to evaluate its expression in kidney tissues. The mechanisms through which the HNF4A P2 isoform inhibits kidney fibrosis were examined by using both in vivo and in vitro models. In this study, we revealed that the sustained downregulation of HNF4A expression was a key characteristic of abnormally repaired PTCs after injury and was associated with cell dedifferentiation. It was confirmed that the HNF4A P2 isoform, rather than the P1 isoform, inhibited TGF-β1-induced PTC dedifferentiation. The activation of fibroblasts, which was induced by dedifferentiated PTCs through paracrine signalling, was also inhibited. In vivo experiments confirmed that HNF4A P2 was more effective than HNF4A P1 was in alleviating kidney fibrosis. Mechanistically, on one hand, HNF4A P2 antagonized the TGF-β1-induced dedifferentiation of PTCs by inhibiting the JAG1/NOTCH pathway. On the other hand, the distinct structure of HNF4A P2 from that of P1 made it unaffected by TGF-β1-activated SRC, allowing HNF4A P2 to perform transcriptional regulatory functions. These findings suggest that targeting the HNF4A P2 isoform could serve as a novel therapeutic strategy to alleviate kidney fibrosis.

To assess the value of multiparametric MRI (mpMRI)-derived radiomic signatures and a combined model for non-invasive prediction of human epidermal growth factor receptor 2 (HER2) expression in bladder cancer (BCa). A total of 113 BCa patients with preoperative pelvic mpMRI were retrospectively enrolled. Radiomic features were extracted from T2WI, DWI, DCE and their combinations. Five machine learning algorithms were used to construct radiomic models. A combined model and a nomogram were developed by integrating radiomic signatures and clinicoradiological variables. The T2WI + DWI + DCE-based RandomForest model achieved the best performance, with an AUC of 0.877 in the training cohort and 0.754 in the validation cohort. Age, risk group, and maximum tumor diameter were independent predictors of HER2 overexpression. The combined model yielded AUCs of 0.808 and 0.870 in the training and validation cohorts, respectively. mpMRI radiomics can non-invasively predict HER2 expression in BCa. The combined nomogram shows good clinical utility, supporting personalized treatment planning for BCa patients.

In this study, an ultrasensitive electrochemiluminescence (ECL) biosensor for folate receptor (FR) has been proposed through the coupling of a cell-free transcription system with duplex-specific nuclease assisted signal amplification strategies. In such system, Ferrocene-labeled ssDNA (Fc-DNA) had been immobilized on tris(2,2 '- bipyridyl) ruthenium(II) chloride hexahydrate-doped SiO2 nanoparticles (Ru@SiO2 NPs) and used as ECL probes. The quenching effect of Fc toward Ru@SiO2 NPs causing the low background ECL signal. 3'-terminal of DNA1 has been modified with folate acid (FA) firstly, target recognition can be realized through specific binding between FR and FA, which allows DNA1 to be protected from hydrolysis catalyzed by Exonuclease I (Exo I). Then DNA1 can be assembled with the genetic circuit to construct a complete DNA transcription template. When combined with T7 RNA polymerase, the recombinant DNA template can drive substantial transcription of large amount of RNA through a cell-free RNA transcription system. The produced RNA can hybridize with Fc-DNA modified on the Ru@SiO2 NPs to form duplex structures, then Fc tags are enzymatically cleaved by duplex-specific nuclease (DSN). This cleavage process is accompanied by RNA releasing and which can be used for cyclic amplification, resulting in the decrease of Fc modified on the nanoparticles and cause the enhancement of the ECL of the system. Without FR, DNA1 had been hydrolyzed and no RNA had been produced through the cell-free transcription system, so the ECL of the system nearly kept unchanged. The ECL response of the system has a linear relationship with FR concentrations ranging from 10 fM to 1 nM under optimized conditions, achieving a detection limit of 3.6 fM (S/N = 3). The synergistic effect of cell-free transcription and DSN assistant amplification enables the ultrasensitive detection of targets. And the terminal protection effect ensures the high selectivity of the developed biosensor. The proposed biosensor has been applied to detect FR in serum samples with satisfied results.

The agile earth satellite scheduling problem (AEOSSP) aims to output reasonable execution plans to manage observation requests and satisfy different user requirements. By analyzing the factors which impact the quality of satellite observation, a specific multi-objective AEOSSP (MO-AEOSSP) is studied, integrating observation profit and average image quality as optimization objectives. To overcome the limitations of traditional iterative methods, we introduce a multi-objective neural policy approach (MONP) which consists of problem decomposition, parameter initialization and subproblem modeling. Through problem decomposition a given MO-AEOSSP can be partitioned into several subproblems, subsequently modeled and trained as encoder- decoder structure neural networks. Various features including the most typical satellite attitude angle are characterized to support the MONP, while parameter transfer initialization is employed to accelerate the overall deep reinforcement learning procedure by leveraging params acquired from optimized subproblem. An end-to-end manner is implemented after all subproblems are trained to output the final non-dominated solutions. Experimental results on various scenarios demonstrate that MONP outperforms four representative multi-objective evolutionary algorithms in terms of metrics including Pareto Front, hypervolume and computational overhead, appearing remarkable ability of convergence, distribution, efficiency and scalability. Experiments further verify the effectiveness of the adopted parameter initialization strategy. To the best of our understanding, this study is an innovative attempt to combine the neural policy approach with MO-AEOSSP considering time-dependent satellite transition.

A multi-scale model is crucial for combining experiments and simulations to reveal the energy storage mechanism. As novel electrode materials, conductive metal-organic frameworks (c-MOFs) provide an ideal platform for understanding the energy storage process in supercapacitors. However, the prevailing circuit models lack consideration of the distinctive transmission path of c-MOFs, which hinders accurate descriptions of c-MOF supercapacitors. By proposing a concept for representing the c-MOF electrode as a crystal-matrix electrode according to the crystallinity, we developed a universal multi-scale circuit model considering crystal shape and porosity to describe the impedance and capacitance of c-MOF electrodes. For supercapacitors with c-MOF electrodes and ionic liquid electrolytes, results predicted from the new multi-scale circuit model, based on microscale parameters obtained from molecular dynamics simulations, demonstrate quantitative agreement with experimental data for electrodes with different crystallinities.

Background:Renal cell carcinoma (RCC) is a prevalent disease within the urinary system, characterized by high mortality rates. Its incidence is increasing each year, posing a significant threat to public health. Currently, the causes of RCC are not fully understood, and there are many limitations in treatment options. Recently, traditional Chinese medicine has gained considerable attention in cancer treatment. Fu Zheng Xiao Yu San Jie Decoction (FZXYSJD) is a well-established treatment for RCC, used for many years at The First Affiliated Hospital of Guangzhou University of Chinese Medicine, and is recognized for its efficacy by both physicians and patients. This study aims to employ methods such as network pharmacology and cell experiments to investigate how FZXYSJD works in treating RCC. Additionally, it seeks to identify new targets that may influence the disease's progression. Methods:First, FZXYSJD drug serum was prepared for cell experiments, including the Cell Counting Kit-8 (CCK-8) assay and colony formation assays. Second, Gene Expression Profiling Interactive Analysis (GEPIA) was applied to explore the specific mechanism of FZXYSJD's suppressive effect in RCC. Western blot analyzed the expression level of E2F transcription factor 5 (E2F5) after treatment with FZXYSJD drug serum in RCC cells. Third, the effect of small interfering RNA targeting E2F5 (siE2F5) on RCC cell proliferation was verified using CCK-8 and colony formation assays. Fourth, the herb identification components of FZXYSJD were retrieved from the Chinese Pharmacopoeia and those components of FZXYSJD that were decocted by high-performance liquid chromatography-mass spectrometry (LC-MS) were detected. Finally, the binding potential of the active ingredient in FZXYSJD to E2F5 was confirmed through molecular docking. Results:FZXYSJD was found to suppress the proliferation of renal clear cell carcinoma cell lines (786O, ACHN) and promote the E2F5 expression. In addition, our findings indicated that low levels of E2F5 expression promoted the proliferation of RCC. Finally, through LC-MS analysis, we found that the components that could be detected after FZXYSJD water decoction were calycosin-7-O-β-D-glucoside, rosmarinic acid, ginsenoside Rg1, liquiritin, glycyrrhizic acid, emodin and polydatin. And the binding potential between those components in FZXYSJD and E2F5 was confirmed through molecular docking. Conclusions:FZXYSJD affects cell proliferation in RCC through the E2F5 gene.

Objective:To investigate the diagnostic utility of a novel Time-Intensity Curve (TIC) parametric imaging technique for improving the accuracy of prostate cancer (PCa) detection. This study aimed to quantitatively assess the technology's impact on the diagnostic performance of ultrasound physicians with disparate levels of clinical experience and to evaluate its potential to standardize diagnostic interpretation. Methods:We conducted a retrospective analysis of 62 patients who underwent transrectal contrast-enhanced ultrasound (TR-CEUS) at Zhangzhou Affiliated Hospital of Fujian Medical University between December 2024 and March 2025. All diagnoses were confirmed by systematic 12-core prostate biopsy. A proprietary TIC parametric imaging software was used to perform a pixel-wise analysis of CEUS cineloops, generating quantitative maps of the perfusion parameter "mean gradient to peak." These maps were then qualitatively classified based on the spatial heterogeneity of perfusion into a four-tier discreteness system. Four junior physicians (1-2 years experience) and four senior physicians (>10 years experience) independently evaluated patient cases, first using conventional grayscale and CEUS images, and then again after a washout period with the addition of the TIC parametric maps. A paired chi-square test compared diagnostic outcomes. Inter-rater and intra-rater reliability were assessed using intra-class correlation coefficients (ICC). Diagnostic performance was evaluated using Receiver Operating Characteristic (ROC) curve analysis, with Area Under the Curve (AUC) as the primary metric. Results:A paired chi-square test demonstrated a statistically significant improvement in diagnostic accuracy when TIC parametric imaging was used as an adjunct to conventional ultrasound (p < 0.0001). The introduction of TIC maps markedly improved intra-group diagnostic consistency; the ICC for junior physicians increased from a good 0.832 to an excellent 0.915, and for senior physicians, it rose from an excellent 0.878 to a near-perfect 0.941. Most notably, the diagnostic performance gap between experience levels was effectively eliminated. The AUC for junior physicians surged from 0.43 (95% CI: 0.36-0.50) to 0.85 (95% CI: 0.79-0.90; p < 0.0001). For senior physicians, the AUC improved from 0.53 (95% CI: 0.46-0.60) to an outstanding 0.95 (95% CI: 0.92-0.99; p < 0.0001). With TIC assistance, the diagnostic efficacy of both junior and senior physicians converged at a high level of performance. Conclusion:TIC parametric imaging, through its ability to objectively quantify and visualize the spatial heterogeneity of tumor blood perfusion, serves as a powerful adjunctive tool that significantly enhances the accuracy and consistency of prostate cancer diagnosis. This technology demonstrates profound clinical value by substantially mitigating the influence of operator experience, thereby shortening the learning curve for novice physicians and standardizing diagnostic quality across all levels of expertise. the sample size is relatively small, which can lead to wide sensitivity confidence intervals and increases the risk of statistical anomalies. require validation in larger, multi-center prospective trials.

Heavy metals (HMs) are hazardous contaminants with persistence and bioaccumulation, attracting widespread attention. Wastewater treatment plants (WWTPs) play vital roles in the pollution control of sewage, closely related to human health and the biological environment. Therefore, eight HMs in three typical WWTPs of Nanjing were determined in this study. The results revealed that Cr, Ni, Cu, and Zn were high-level HMs in all WWTPs. Notably, the highest contents of high-level HMs were found in electroplating WWTP (EWWTP) influent among three WWTPs, probably causing their higher removal (19.34-55.32%) during their primary treatment. In contrast, most HMs could be removed in the secondary treatment stage of municipal WWTP (MWWTP) and industrial WWTP (IWWTP) with the highest removal of As (72.00-85.81%). Analogously, nutrients were mainly removed during the secondary stage, with superior performance in MWWTP. A decrease in HMs removal was observed in the tertiary treatment of MWWTP and IWWTP compared to the secondary stage, while higher HMs removal (0.51-29.15%) was found in EWWTP except Hg. The highest content of HMs in sludge was Zn and Cr, which was more abundant in EWWTP than other WWTPs. The results of Illumina Miseq sequencing demonstrated the inhibition of microbial richness and diversity of EWWTP and IWWTP by industrial wastewater. Besides, alterations of microbial community structure and components were also observed owing to various influent sources. More similarity was found between EWWTP and MWWTP, in which the abundance of dominant genera, including Saccharimonadales (7.60-9.56%), Raineyella (5.06-7.38%), and Thauera (2.48-4.45%) was much higher than IWWTP.

The Agile Earth Observation Satellite Scheduling Problem (AEOSSP) is a complex, time-dependent combinatorial optimization challenge that has recently attracted significant attention. Existing methods often struggle to balance the solution timeliness and quality. To address this issue, we propose the Differential Evolution Ensemble Method (DEEM), a novel framework that simultaneously ensures timeliness and high- quality solutions. DEEM is an ensemble learning framework crafted to optimize robust and adaptable heuristics for AEOSSP, exploiting the diverse applicability of different heuristics across various scenarios. In DEEM, task value vectors are formulated for each heuristic based on their respective task priority sequences. These vectors are integrated into a final ensemble value vector through an ensemble weight vector. The resulting ensemble task value vector is then converted into a task priority sequence and processed by a Scheduling Module. This Ensemble Heuristic Process (EHP) enables the effective exploitation of the applicabilities of individual heuristics. The ensemble weight vector is iteratively optimized using the Differential Evolution (DE). Moreover, DEEM employs a Knowledge Transfer Strategy, using knowledge from pre-tested scenarios to guide the initialization of the ensemble weight vector for new scenarios. This initial transferred DEEM resulted in the DEEM-0 variant, an ensemble construction heuristic that rapidly delivers high-quality outcomes. Experimental results demonstrate that DEEM surpasses the state-of-the-art AEOSSP meta-heuristic in both solution quality and speed across all tested problem scales. Notably, DEEM-0, as a construction heuristic, also outperforms the state-of-the-art meta-heuristic in a construction timeframe. Additional experiments validate the effectiveness of our proposed EHP, DE, and Knowledge Transfer Strategy. Therefore, these findings highlight the efficacy and user-friendly of DEEM in both constructive processes and continuous optimization for satellite scheduling systems. The results not only advance the state-of-the-art in satellite scheduling but also provide a robust framework that can be adapted to complex optimization problems in engineering and operations research, demonstrating its broader applicability beyond the original domain.