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To investigate the potential retinal and choroidal markers in identifying cerebral small vessel disease (CSVD) through Swept Source Optical Coherence Tomography Angiography (SS-OCTA). Participants were enrolled from the FRESH-CSVD study, and underwent multimodal magnetic resonance imaging (MRI) and SS-OCTA. CSVD patients were identified on multimodal MRI based on STRIVE-2 criteria. SS-OCTA parameters were captured using the VG200I device. The association between SS-OCTA parameters and CSVD imaging features was analyzed based on a least absolute shrinkage and selection operator (LASSO)-derived Logistic regression model. Subgroup analyses were conducted to explore the potentially applicable populations for identifying CSVD through SS-OCTA markers. 170 eyes (101 CSVD patients) were included. Periventricular white matter hyperintensities (PWMHs), enlarged perivascular spaces (EPVS), lacunes and CSVD burden score were all associated with OCTA parameters (all P < 0.05). In the hypertension subgroup, OCTA markers still showed associations with CSVD imaging features (all P < 0.05). SS-OCTA revealed widespread alterations of fundus blood flow in CSVD patients, especially in deep microvasculature. Relationships between SS-OCTA markers and CSVD imaging features were consistent to be detected across hypertensive populations, suggesting the potential for early CSVD screening using SS-OCTA in this population.

Efferocytosis, the process by which phagocytes like macrophages and dendritic cells clear apoptotic cells, is crucial for maintaining tissue homeostasis. However, its function in bladder cancer (BLCA) remains unclear and warrants further exploration. This study seeks to establish a prognostic and treatment response signature based on efferocytosis-related genes (EFRGs) for bladder cancer patients. BLCA-related datasets were sourced from the Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/ ) and the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/ ). A comprehensive analysis was performed on 28 prognostic EFRGs. Clustering analysis was carried out using ConsensusClusterPlus. Prognostic differentially expressed genes (DEGs) were identified based on expression variations across the subtypes. A prognostic model was subsequently developed using least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression. Lastly, a thorough analysis was conducted to explore the relationship between risk scores and the tumor immune microenvironment, somatic mutations, as well as responses to immunotherapy and chemotherapy. Consensus clustering revealed two efferocytosis subtypes, Cluster A and Cluster B, and identified 61 prognostic DEGs between them. A risk scoring model, incorporating four key DEGs—SERPINE2, DPYSL3, CTSE, and KRT16—was constructed and validated. This model successfully stratified patients into high-risk and low-risk groups, with high-risk patients showing worse prognosis, increased immune infiltration, and higher immune checkpoint gene expression. The risk scores also provide insights into patient responsiveness to treatment. In conclusion, we identified four key genes—SERPINE2, DPYSL3, CTSE, and KRT16—that can be used to develop a prognostic model for bladder cancer. These findings may provide valuable molecular targets for the clinical diagnosis and therapeutic strategies of bladder cancer.

Ischemic stroke impacts glymphatic function, but its role in prognosis remains unclear. This study evaluated glymphatic function in 146 participants, including non-stroke (healthy controls, n = 48; nonvascular cognitive impairment patients, n = 47) and ischemic stroke cohorts (n = 51). The bilateral diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) index, choroid plexus (CP), and perivascular space (PVS) volume ratio, which represent the glymphatic system, were compared across two cohorts and between pre-rehabilitation (Time 1) and 30 days post-rehabilitation (Time 2). Post-stroke cognitive impairment (PSCI) was characterized as enduring cognitive deficits persisting six months after a stroke. Stroke patients exhibited significantly lower bilateral DTI-ALPS index compared with the non-stroke population ( P  < 0.05), while the infarct side showed post-rehabilitation improvement ( P  < 0.05). The DTI-ALPS index of infarct side at Time 1 did not predict poor outcome but was correlated with 6-month PSCI ( P  < 0.05). These results indicate that ischemic stroke diminishes glymphatic function, partially recovering post-rehabilitation, and suggest that the DTI-ALPS index could serve as a predictor for cognitive impairment following ischemic stroke.

The timely identification of individuals at high risk for peptic ulcers (PUs) is vital in preventing gastrointestinal bleeding after antiplatelet therapy. This study was designed to determine PU risk factors and develop a risk assessment model for PU detection in the general Chinese population. In a prospective dataset, clinical data from individuals undergoing gastroscopic evaluation between April 2019 and May 2022 were recorded. PUs were defined as mucosal defects exceeding 5 mm confirmed via gastroscopy. Participants were categorized into development (April 2019 to April 2021) and validation (May 2021 to May 2022) sets based on chronological order. LASSO-derived logistic regression analysis was employed to create a score, which was further validated via temporal validation. A total of 902 patients were ultimately enrolled, 204 (22.6%) of whom had PUs based on endoscopic findings. In the development cohort (n = 631), seven independent risk factors emerged: male sex (OR = 2.35, P  = 0.002), white blood cell (WBC) count (OR = 1.16, P  = 0.010), red blood cell (RBC) count (OR = 0.49, P  < 0.001), globulin level (OR = 0.92, P  = 0.004), albumin level (OR = 0.94, P  = 0.020), pepsinogen I (PGI) level (OR = 1.01, P  < 0.001), and positive Helicobacter pylori (HP) antibody (OR = 2.50, P  < 0.001). Using these factors, a nomogram (HAMPROW score [hazard ratio (HP) antibody, albumin, male, PGI, RBC, globulin, and WBC]) was developed for individual PU prediction. The ability of the HAMPROW score to predict survival was confirmed with AUCs of 0.854 (95% CI 0.816–0.891) and 0.833 (95% CI 0.771–0.895) in the development and validation sets, respectively. In conclusion, the HAMPROW score can be used to screen for PUs effectively in the general Chinese population, facilitating personalized early detection of high risk of gastrointestinal bleeding before antiplatelet therapy.

Ore is a crucial component in the process of industrialization, and its crushing is a practice that is inextricably linked to our society. This study aims to simulate the crushing process of minerals in a comminution device using the Discrete Element Method (DEM) by characterizing the bonding parameters of mineral particles. Utilizing the EDEM software (2018) for discrete element simulations, the study investigated the influence of bonding parameters on the compressive strength and other performance indicators of the particle bonding model. The study was executed through the application of a Box–Behnken Design (BBD) and Response Surface Methodology (RSM), which facilitated the construction of a second-order response surface regression model. The optimal values for normal stiffness per unit area, shear stiffness per unit area, critical normal stress, and critical shear stress were meticulously determined. The subsequent simulation experiments strongly verify the feasibility of the proposed characterization method for key parameters.

Objective In endovascular therapy (EVT) for stroke, futile recanalization contributes to poor prognosis. Mild hypercapnia may enhance cerebral blood flow and prevent ischemia, but its impact on prognosis in successfully recanalized EVT patients is unclear. Methods We retrospectively analyzed 237 patients from the INSPIRE database who underwent successful recanalization at our center (October 2018–March 2022). Patients were grouped by post‐EVT PaCO2 levels: high (40–50 mmHg) and low (<40 mmHg). Significant infarct expansion (SIE) was defined as a decrease in ASPECTS or pc‐ASPECTS by ≥2 from initial to 3–5 days post‐EVT. Poor outcome was modified Rankin Scale score 3–6 at 90 days. Results High PaCO2 was negatively associated with SIE (OR 0.42, 95% CI 0.22 to 0.84) and poor outcome (OR 0.42, 95% CI 0.20 to 0.87). Mediation analysis showed a significant total effect of high PaCO2 on poor outcome (coefficient −0.192, 95% CI −0.345 to −0.046), including an indirect effect mediated by SIE (coefficient −0.055, 95% CI −0.10 to −0.006). These associations were consistent in anterior circulation stroke and patients without severe low PaCO2 (<30 mmHg). Conclusion Maintaining mild hypercapnia (PaCO2 40–50 mmHg) after EVT may prevent poor outcomes by reducing post‐EVT infarct progression. Maintaining mild hypercapnia (PaCO2 40–50 mmHg) within the initial 24 h after endovascular therapy may prevent poor outcomes by reducing post‐EVT infarct progression.

Background Mounting evidence highlights the critical role of mitochondrial dysfunction, driven by mitochondrial-related genes (MTRGs), in the development, progression, and therapeutic response of cancer. However, a comprehensive analysis linking specific Mitochondria-Related Gene Signature (MTRGS) to Bladder Cancer (BLCA) prognosis and immunotherapy efficacy remains largely unexplored. Therefore, this study aims to investigate the role of MTRGs in BLCA, construct and validate a novel MTRGs-based prognostic signature, and explore its potential for guiding personalized treatment strategies. Materials and methods Leveraging transcriptomic and clinical data from The Cancer Genome Atlas (TCGA-BLCA) cohort, we constructed a mitochondrial-related risk score model using LASSO, univariate and multivariate Cox regression analyses. This model was subsequently validated in an independent Gene Expression Omnibus (GEO) dataset. We then employed integrated bioinformatics approaches (implemented in R with online databases) to characterize features of the tumor microenvironment (TME), immune cell infiltration, Gene Set Enrichment Analysis (GSEA), tumor mutational burden (TMB), and drug sensitivity across different risk groups. Additionally, using data from public databases, we further verified our findings through single-cell RNA sequencing (scRNA-seq) analyses. Results Using 104 mitochondria-related differentially expressed genes (MTR-DEGs), unsupervised non-negative matrix factorization (NMF) clustering stratified BLCA patients into three molecular subtypes (Clusters 1–3). Survival analysis revealed that patients in Cluster 3 had significantly longer overall survival than those in Clusters 1 and 2. Our mitochondrial-related risk model incorporating six core genes ( MAP1B , PYCR1 , HSD3B1 , KLK6 , AKR1B15 , and TAT) - exhibited robust prognostic capability (3-years AUC = 0.695 in TCGA-BLCA, 0.798 in GEO-GSE32894, 0.703 in GSE13507). The risk model revealed distinct immune infiltration patterns between high- and low-risk groups. Furthermore, Tumor Immune Dysfunction and Exclusion (TIDE) and immunophenotype score (IPS) analyses demonstrated that integrating risk scores with stromal/immune signatures significantly enhanced immunotherapy benefit prediction across BLCA risk-subgroups. Crucially, the model demonstrated predictive power for therapy response: low-risk patients showed potential benefit from immune checkpoint inhibitors, while high-risk patients exhibited heightened sensitivity to specific chemotherapy agents or targeted therapies (e.g., Tozasertib, Gemcitabine) and may require intensified regimens. Conclusion This validated mitochondrial risk model delivers a clinically actionable biomarker for BLCA prognosis stratification and guides personalized therapeutic selection, enabling precision treatment intensification.

To develop and validate markers for screening mild cognitive impairment (MCI) in cerebral small vessel disease (CSVD) using Swept Source Optical Coherence Tomography Angiography (SS-OCTA). Participants with MCI and normal cognition (NC) underwent structural magnetic resonance imaging (S-MRI) and SS-OCTA were prospectively recruited (Dream-10 and FRESH-CSVD study, NCT06164262 and NCT06431711). Patients with Alzheimer's disease (AD) were excluded according to plasma biomarkers test. MCI was defined as a Montreal Cognitive Assessment (MoCA) score ranging from 18 to 26 points, accompanied by a complaint of memory loss. Participants were categorized into development (January 2024 to May 2024) and validation cohorts (June 2024 to September 2024) based on chronological order. LASSO-derived logistic regression analysis was employed to filter potential markers, which was further validated via temporal validation. A total of 102 participants were enrolled, with 59.8% (61/102) having MCI. In the development cohort (n = 61), the volume of left transverse temporal gyrus (L-TTG), the volume of right choroid plexus (R-CP) on S-MRI, 0-3mm and 0-6mm oculus Sinister (OS) macular perfusion area (PA) of choriocapillaris (CC) were identified as MCI markers (FDR p < 0.05) (Table 1). In the validation cohort (n = 41), 0-3mm and 0-6mm OS macular CCPA were identified as superior MCI markers (AUC:0.849 and 0.833; sensitivity:0.672 and 0.639; specificity:0.976 and 0.951) with significant better NRI and IDI when compared to other MCI markers (all p < 0.05) (Table 2). SS-OCTA, especially OS macular CCPA, holds promise for screening MCI in CSVD patients.

Background To develop and validate markers for screening mild cognitive impairment (MCI) in cerebral small vessel disease (CSVD) using Swept Source Optical Coherence Tomography Angiography (SS‐OCTA). Method Participants with MCI and normal cognition (NC) underwent structural magnetic resonance imaging (S‐MRI) and SS‐OCTA were prospectively recruited (Dream‐10 and FRESH‐CSVD study, NCT 06164262 and NCT06431711). Patients with Alzheimer's disease (AD) were excluded according to plasma biomarkers test. MCI was defined as a Montreal Cognitive Assessment (MoCA) score ranging from 18 to 26 points, accompanied by a complaint of memory loss. Participants were categorized into development (January 2024 to May 2024) and validation cohorts (June 2024 to September 2024) based on chronological order. LASSO‐derived logistic regression analysis was employed to filter potential markers, which was further validated via temporal validation. Result A total of 102 participants were enrolled, with 59.8% (61/102) having MCI. In the development cohort (n = 61), the volume of left transverse temporal gyrus (L‐TTG), the volume of right choroid plexus (R‐CP) on S‐MRI, 0‐3mm and 0‐6mm oculus Sinister (OS) macular perfusion area (PA) of choriocapillaris (CC) were identified as MCI markers (FDR p < 0.05) (Table 1). In the validation cohort (n = 41), 0‐3mm and 0‐6mm OS macular CCPA were identified as superior MCI markers (AUC:0.849 and 0.833; sensitivity:0.672 and 0.639; specificity:0.976 and 0.951) with significant better NRI and IDI when compared to other MCI markers (all p < 0.05) (Table 2). Conclusion SS‐OCTA, especially OS macular CCPA, holds promise for screening MCI in CSVD patients.

Abstract The consensus algorithm is a key blockchain technology that is used to ensure consistency and reliable consensus without centralized management. However, current consensus algorithms do not fully consider the facts that the faulty probability of nodes is variable in real applications, especially the lower faulty probability in permissioned blockchains. They have excessive communication cost and low throughput in the face of high-performance demand scenarios such as supply chain traceability and the settlement of financial asset transactions. Thus, this paper proposes an efficient double-layer consensus algorithm based on a variable faulty probability model which is Byzantine fault tolerant. It adopts a double-layer structure that incorporates multiple primary nodes to improve scalability and performance. Compared with the multilayer structure, the double-layer structure can dynamically adjust the consensus mechanism more conveniently and flexibly. Furthermore, we select the appropriate grouping strategy and change the number of consensus participants according to the faulty probability distribution of the nodes, so that the consensus process is optimized and the system consensus efficiency is significantly improved. The communication complexity of the proposed algorithm is only the 1.33rd power of the total number of nodes. The experimental results show that our algorithm has a lower communication overhead, higher throughput, and scalability than the state-of-the-art when the node faulty probability is low. Graphical Abstract Graphical Abstract Problems, Design and Contribution