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Decreased potential for lymphatic vessel generation is a hallmark of early diagnosed arterial hypertension and can be reversed by treatment with angiotensin converting enzyme inhibitors
Joint developmental trajectories of anxious/depressive symptoms and aggressive behavior in Chinese adolescents: The roles of contextual, personality, and cognitive factors
Association of surgical resection with survival in retroperitoneal leiomyosarcoma based on SEER propensity score matching and machine-learning models
Abstract Retroperitoneal leiomyosarcoma (RLS) is a rare and aggressive subtype of soft tissue sarcoma with limited population-level evidence guiding surgical decision-making. This study aimed to assess the prognostic value of surgery in patients with RLS using a large real-world cohort and advanced analytical methods. Patients diagnosed with RLS between 2000 and 2019 were identified from the Surveillance, Epidemiology, and End Results (SEER) database. Propensity score matching (PSM) was used to balance baseline variables. Overall survival (OS) and cancer-specific survival (CSS) were analyzed using Kaplan–Meier curves and Cox proportional hazards models. Random survival forests (RSF) were applied to evaluate variable importance and model robustness. A total of 1041 patients were included, of whom 817 (78.5%) underwent surgery. Before matching, significant imbalances were observed in age, grade, and SEER stage. After 1:1 PSM (159 matched pairs), covariate balance was substantially improved. Surgery was associated with significantly improved survival (OS: HR = 0.34, 95% CI: 0.26–0.45; CSS: HR = 0.34, 95% CI: 0.25–0.46; both P < 0.001). High-grade tumors and advanced SEER stage remained independent adverse prognostic factors. RSF consistently ranked surgery, stage, and grade as the most important predictors of survival. Surgical resection status was strongly associated with survival in SEER-based analyses, but this association is subject to substantial unmeasured confounding by resectability, anatomic extent, and patient fitness; therefore, results should be interpreted as prognostic rather than causal and highlight the need for multidisciplinary assessment in high-volume sarcoma centers.
Therapeutic Potential of GYY4137 in Reducing Oxidative Stress and Mortality in Experimental Decompression Sickness
Critical role for a high-plasticity cell state in lung cancer
Phytosociological analysis and floristic composition of fabaceae species assessing human impact and edaphic variables
Study on the spatiotemporal evolution characteristics and simulation prediction of urban metabolic efficiency in China’s urban agglomerations
Hydrogen storage potential of cubic InXH3 (X = Be, Mg, Ca, Sr, Ba, Ra) hydride perovskites: a comprehensive first principles investigation
Non-albumin proteinuria is associated with all-cause mortality in community-dwelling adults
Quantitative EEG signatures of power and functional connectivity alterations in Alzheimer’s disease and frontotemporal dementia
Brain mysteries and Bronze Age diplomacy: Books in brief
A unified framework for correcting batch effects and integrating multi-omics data
Abstract Multi-omics studies enable a comprehensive understanding of biological systems by integrating complementary molecular layers such as gene expression, DNA methylation, and chromatin accessibility. However, the generation of multi-omics data remains costly and labor-intensive, leading researchers to combine publicly available datasets collected from different cohorts, laboratories, and platforms. Integrating such heterogeneous datasets introduces substantial batch effects and technical variability that can obscure true biological structure. While numerous batch correction methods exist for single-omics data, systematic approaches for multi-omics batch effect correction remain limited. Correcting each omics layer independently risks disrupting cross-omics concordance and fails to ensure that samples are aligned within a unified multi-modal space, underscoring the need for coordinated, modality-aware harmonization that preserves shared molecular structure while removing technical variation across studies. To address this gap, we developed MoDAmix, a unified framework that leverages domain adaptation to remove technical variation while preserving shared molecular structure across omics layers. In particular, MoDAmix aligns feature distributions across batches and modalities through adversarial learning, enforcing consistency both within and between omics types to achieve coherent cross-omics integration. MoDAmix proceeds through four stages: (1) pre-training to learn initial feature representations, (2) adversarial adaptation to reduce batch effects within each omics type, (3) multi-omics adversarial alignment to harmonize modalities in a shared latent space, and (4) semi-supervised class alignment to refine subtype separability through pseudo-labeling and centroid consistency. Evaluations on both single-cell and bulk datasets–including mouse brain (gene expression and chromatin accessibility) and cancer cohorts (gene expression and DNA methylation)–demonstrated that MoDAmix effectively mitigates batch effects, improves clustering and classification performance, and preserves subtype structure across domains. Together, these results highlight MoDAmix as a robust framework for multi-omics batch effect correction and integration, enabling reliable cross-cohort analysis in systems biology and precision medicine. MoDAmix is publicly available at https://github.com/cbi-bioinfo/MoDAmix.
Diverse foraging strategies of an avian apex predator in an old-growth forest
Probabilistic operational management of a renewable-based microgrid considering uncertainties using the self-adaptive gravitational search algorithm
Abstract Increasing uncertainties in electricity prices, load demand, and renewable energy generation pose significant challenges for optimal microgrid operation in deregulated electricity markets. This paper proposes a self-adaptive Gravitational Search Algorithm (SGSA), which enhances the standard GSA by incorporating a self-adaptive mutation operator with two movement strategies to mitigate premature convergence and improve solution quality. To model uncertainties in load demand, market prices, and renewable outputs, the 2 m-Point Estimation Method (PEM) is employed as a computationally efficient alternative to conventional stochastic approaches. The proposed SGSA-PEM framework is applied to a low-voltage microgrid consisting of microturbines, phosphoric acid fuel cells, photovoltaic units, wind turbines, and battery storage. Simulation results indicate that the integration of battery storage reduces the total generation cost by up to 49.7%, while renewable energy penetration increases by approximately 10% during peak demand periods. Furthermore, comparative analysis shows that SGSA achieves lower operating costs and converges about 25% faster than standard GSA and Particle Swarm Optimization (PSO). The results confirm that the proposed framework provides a computationally efficient and robust solution for probabilistic microgrid energy management under uncertainty.
Lipidomic profiling identifies key pathways and a 5-lipid panel with high diagnostic efficacy for ischemic stroke
Abstract Ischemic stroke (IS) accounts for over 80% of all stroke cases, presenting as a prevalent, debilitating cerebrovascular disorder with limited therapeutic options. The urgent need for early diagnostic biomarkers and insights into pathogenesis has highlighted dysregulated lipid metabolism as a key contributor, while metabolomics advances enable novel biomarker exploration. This study integrated bioinformatics and a case-control design to investigate IS-related lipid metabolism pathways and blood lipid biomarkers. Gene Expression Omnibus (GEO) gene expression datasets were analyzed via Gene Set Enrichment Analysis (GSEA) to identify lipid pathways, and case-control analyses employed Chi-square/Z tests for conventional blood lipids, Liquid Chromatography-Mass Spectrometry (LC-MS) for plasma small-molecule lipids, and orthogonal partial least squares discriminant analysis, t-tests, and Receiver Operating Characteristic (ROC) curves for validation. Results revealed five significantly downregulated lipid pathways (α-linolenic acid, linolenic acid, ether lipid, glycerophospholipid, and sphingolipid metabolism). IS patients exhibited dyslipidemia (elevated TC/TG/LDL-C, reduced HDL-C). Additionally, 15 differentially expressed lipid molecules were identified in a validation cohort after excluding the influence of comorbidities. Among these, five representative lipids (e.g., PE(P-18:1/22:4)) demonstrated potential diagnostic performance, with an area under the receiver operating characteristic curve (AUC) of 0.917, sensitivity of 60.0%, and specificity of 96.7%, indicating their potential utility as biomarkers for the early detection of IS.
Short term real-world effectiveness of faricimab in neovascular age-related macular degeneration patients in the Republic of Korea
Abstract To analyze visual and anatomic outcomes of faricimab injection in neovascular age-related macular degeneration (nAMD) in a real-world setting in Korea. We collected data from nAMD patients who received faricimab injection from 2024 to 2025. The past injection history, and faricimab injection history was obtained. Visual acuity (VA) and central macular thickness (CMT) outcomes for 1, 3, 6, 12 months after initial faricimab injection were compared to baseline, and also compared between sex, different age-groups, and naïve vs. non-naïve groups. A total of 286 patients, 293 eyes from 8 university hospitals were included in this study. VA improved from baseline 58.9 ± 17.1 letters to 60.7 ± 23.5, 61.0 ± 18.1, 59.9 ± 19.3, 60.5 ± 18.2 letters at 1, 3, 6, 12 months respectively (statistically significant at 3 months, p = 0.009), while CMT improved by -60.6 ± 89.0, -55.9 ± 87.3, -46.5 ± 97.5, -70.7 ± 97.8 μm compared to baseline ( p < 0.001 at all timepoints). An age group analysis showed that the youngest age group (< 60 years) had superior VA results, while the naïve group showed superior outcomes compared to the non-naïve group. CMT fluctuation showed inverse correlation with visual acuity at 6 months, with lower CMT fluctuation being associated with the young and naïve. Faricimab injection for nAMD, in a real-world setting in the Republic of Korea, showed significant functional and anatomic improvements with superior results in the younger age group and naïve group, suggesting promising effectiveness of a bispecific blockage of VEGF-A and Ang-2 at the initial phases of nAMD.
Diagnosis model of early malignant pulmonary nodules based on clinical laboratory data
Common variation in meiosis genes shapes human recombination and aneuploidy
Abstract The leading cause of human pregnancy loss is aneuploidy, often tracing to errors in chromosome segregation during female meiosis 1,2 . Although abnormal crossover recombination is known to confer risk for aneuploidy 3,4 , limited data have hindered understanding of the potential shared genetic basis of these key molecular phenotypes. To address this gap, we performed retrospective analysis of pre-implantation genetic testing data from 139,416 in vitro fertilized embryos from 22,850 sets of biological parents. By tracing transmission of haplotypes, we identified 3,809,412 crossovers, as well as 92,485 aneuploid chromosomes. Counts of crossovers were lower in aneuploid versus euploid embryos, consistent with their role in chromosome pairing and segregation. Our analyses further revealed that a common haplotype spanning the meiotic cohesin SMC1B is associated significantly with both crossover count and maternal meiotic aneuploidy, with evidence supporting a non-coding cis -regulatory mechanism. Transcriptome- and phenome-wide association tests also implicated variation in the synaptonemal complex component C14orf39 and crossover-regulating ubiquitin ligases CCNB1IP1 and RNF212 in meiotic aneuploidy risk. More broadly, variants associated with aneuploidy often showed secondary associations with recombination, and several also exhibited associations with reproductive ageing traits. Our findings highlight the dual role of recombination in generating genetic diversity, while ensuring meiotic fidelity.