Search Results (1,752)

Primary neuronal cultures from the brain are critical for investigating disease-specific cellular and molecular mechanisms in mouse models. Current methods for obtaining primary cultures require embryonic brains that are affected by embryonic lethality and genotypic characterization in severe disease models such as sickle cell disease (SCD). Furthermore, these neuronal cultures require about 14 days in vitro (DIVs) for neurite outgrowth to mature. We adapted and optimized a relatively simplified and reproducible method using brains from postnatal day 1 mouse pups for isolating and culturing hippocampal and cortical neurons. This approach produces viable neurons that attach, extend neurites, and express key synaptic markers by 7 DIV and also minimizes glial outgrowth. We successfully applied this approach to isolating and culturing hippocampal and cortical neurons from the brains of one-day-old (P1) pups of humanized transgenic homozygous BERK sickle cell and control mice. Morphological observations at 3, 7, and 14 DIVs demonstrated robust neuronal attachment, neurite outgrowth, and overall structural development in both male and female hippocampal and cortical neurons. Neurons in culture expressed key markers including neuronal nuclear protein (NeuN/Rbfox3), neurofilament 200 (NF200), microtubule-associated protein 2 (MAP2), vesicular glutamate transporter 1 (VGLUT1), postsynaptic density protein 95 (PSD 95), and glutamate N-methyl-D-aspartate receptor subunit 2B (GluN2B). Notably, male SCD hippocampal neurons evinced a higher density of PSD 95 puncta on dendritic spines compared to controls on 7 as well as 14 DIVs. Incubation of male hippocampal neurons in a sickle cell-like microenvironment with TNF-α and heme further increased the density of PSD 95 puncta and colocalization of GluN2B with PSD 95, supporting the utility of this culture system for examining disease-relevant structural and molecular responses. This optimized culture system provides a simplified and reproducible platform to investigate the mechanisms involving neuronal dysfunction in challenging mouse models of brain disorders. Full article

Hypertrophic Cardiomyopathy (HCM) is one of the most common inherited cardiomyopathies and remains an important cause of ventricular arrhythmias and sudden cardiac death (SCD), particularly in younger individuals. Although the annual incidence of arrhythmic death is relatively low in contemporary cohorts, identifying those patients who may benefit from primary prevention with an implantable cardioverter-defibrillator (ICD) remains a major clinical challenge. Current risk stratification strategies rely on two principal paradigms. The European approach is centered on the HCM Risk-SCD score, whereas the American approach is mainly based on major clinical risk markers. Both strategies have important strengths and limitations, reflecting the persistent difficulty of accurately predicting arrhythmic events in such a heterogeneous disease. The HCM Risk-SCD score has demonstrated robust external validation and high specificity for identifying patients at higher risk, but it may fail to recognize some vulnerable individuals who remain below conventional treatment thresholds. For this reason, several additional risk modifiers have gained increasing relevance in contemporary practice. Among them, extensive late gadolinium enhancement, left ventricular systolic dysfunction, apical aneurysm, and clinically meaningful genetic findings may provide important incremental prognostic information beyond traditional models. Emerging disease-modifying therapies, in particular Mavacamten, may also influence future risk assessment. However, whether these improvements translate into a true reduction in SCD risk remains uncertain. Importantly, the decision to implant an ICD should not depend on numerical risk alone. It should arise from a process of shared decision-making integrating estimated risk, treatment burden, competing comorbidities, age, lifestyle, and patient values. In this context, the concept of an individualized threshold of “acceptable risk” becomes central. In conclusion, prevention of SCD in HCM is moving beyond conventional scores toward a personalized and dynamic framework in which predictive tools, advanced phenotyping, evolving therapies, clinical expertise, and patient preferences are combined to guide individualized care. Full article

Background: Osteosarcoma remains the most common primary malignant bone tumor in children and adolescents, with largely unchanged survival outcomes in recent decades. Non-invasive biomarkers for preoperative risk stratification are urgently needed. Circulating immune checkpoint proteins (ICPs) and bone remodeling factors (BRFs) represent promising candidates; however, their combined prognostic value in osteosarcoma remains unclear. Methods: We prospectively analyzed plasma samples from 47 patients with osteosarcoma, selecting the earliest available sample before disease progression or the last follow-up. A panel of ICPs and BRFs was quantified using multiplex immunoassays. sHVEM and sCD27 were used to construct a two-marker ICP signature (ICP2); subtypes were defined by unsupervised consensus clustering (k = 2) applied to log₂-transformed sHVEM and sCD27 concentrations, and sOPN was included for integrative analysis. Survival associations were assessed using Kaplan–Meier analysis and Firth’s penalized Cox regression. The model performance was evaluated using Harrell’s C-index. Results: Two ICP2 subtypes were identified (type1, n = 26; type2, n = 21). ICP2-type2 was associated with significantly worse progression free survival (PFS) (p = 0.0045) and overall survival (OS) (p = 0.0024) and remained an independent predictor after adjusting clinicopathological factors. The ICP2 model demonstrated high discriminative performance (C-index 0.924 for PFS and 0.903 for OS). Internal validation by leave-one-out cross-validation (LOOCV), in which consensus clustering was re-derived at each fold, yielded corrected C-indices of 0.612 for PFS and 0.806 for OS (versus apparent estimates of 0.687 and 0.689, respectively), confirming acceptable-to-good discrimination after accounting for overfitting. Elevated sOPN was associated with poorer survival in the exploratory analyses, although its prognostic value was less consistent under a pre-specified cutoff. In the combined models, sOPN provided modest additional value for OS stratification, with the grdgfdgICP2-type2/OPN-high subgroup exhibiting the poorest outcome. Conclusions: The circulating sHVEM/sCD27 signature defines prognostically distinct osteosarcoma subtypes with a strong discriminative performance. This minimally invasive model may support preoperative risk stratification. Integration with sOPN suggests a potential biological link between immune regulation and bone remodeling, warranting validation in larger cohorts. Full article

Achieving efficient and high-quality surface processing of single-crystal diamond (SCD) remains challenging due to its extreme hardness and chemical inertness. Traditional Fenton-based slurries using H₂O₂ suffer from poor stability, safety risks, and strict acidic pH requirements. In this study, peroxymonosulfate (PMS) is introduced as an alternative oxidant to develop a novel chemical mechanical polishing (CMP) slurry for SCD. Compared with H₂O₂, PMS exhibits higher stability and generates sulfate radicals (SO₄⁻·) with stronger oxidation capability when activated by Fe²⁺. The proposed slurry achieves efficient material removal over a wider pH range (2–6). Under optimal conditions (pH = 3), a maximum material removal rate (MRR) of 676 nm/h is obtained, along with an ultra-smooth surface (Sa = 0.177 nm in the measuring area of 868 × 868 μm²). Notably, the slurry maintains high MRR (>400 nm/h) even under weakly acidic conditions (pH 5–6). XPS and radical quenching experiments confirm that continuous generation of reactive radicals promotes surface oxidation and stable material removal. This work provides a stable and efficient CMP slurry for SCD with enhanced pH adaptability. Full article

Primula nutans Georgi, a medicinal herb used in Mongolian and Tibetan medicine for treating respiratory ailments, is a natural agent with antiobesity potential. We investigated the antiobesity and insulin-sensitizing effects of P. nutans Georgi extract (PGE) using in vitro and in vivo models. In 3T3-L1 preadipocytes, PGE inhibited adipocyte differentiation and lipid accumulation without cytotoxicity, accompanied by the reduced expression of adipogenic transcription factors (PPARG, C/EBPA, and adiponectin) and lipogenic genes (FASN, SCD1, and ACC), particularly during the early stages of adipogenesis. Similar effects were observed in primary stromal vascular cells derived from mouse inguinal white adipose tissue. PGE upregulated C/EBP homologous protein and C/EBPB and was associated with altered cell cycle progression, increased G2/M phase distribution, and the potential disruption of mitotic clonal expansion during early adipogenesis. In HFD-induced obese mice, intraperitoneal administration of PGE (10 or 30 mg/kg) significantly reduced body weight gain, white adipose tissue mass, and hepatic steatosis, independent of food intake. PGE downregulated lipogenic and proinflammatory gene expression in adipose and hepatic tissues and increased AMPK phosphorylation in white adipose tissue. PGE improved glucose tolerance and was associated with enhanced insulin sensitivity, as evidenced by reduced areas under the curve in the glucose tolerance and insulin tolerance tests and increased circulating adiponectin levels. Feature-based molecular networking identified 61 compounds from PGE. Network pharmacology analysis revealed several antiobesity targets, including PPARG and AKT1. Molecular docking analyses suggested favorable binding affinities between major compounds and metabolic regulators. Collectively, these findings suggest that PGE may suppress adipogenesis and improve metabolic parameters in obese mice, supporting its potential as a natural candidate for obesity and related metabolic disorders. Full article

Heat stress (HS) is among the most economically consequential environmental challenges to global dairy production, causing progressive declines in milk yield, compositional quality, and mammary cellular integrity. The temperature–humidity index (THI) is the primary thermal load metric, with performance-impairment thresholds typically beginning at THI 68 in Holstein cattle, with severe impacts manifesting beyond THI 72; breed-specific thresholds for Jersey, Brown Swiss, and Simmental cows differ owing to their lower metabolic heat load and greater inherent thermotolerance. At the molecular level, HS activates heat shock protein networks—notably HSPA1A, HSP90B1, and HSPH1—through HSF1/HSF4 transcriptional activation, while simultaneously suppressing casein genes (CSN1S1, CSN2, CSN3), lipogenic genes (FASN, SCD, CD36), amino acid transporters (SLC7A5, SLC38A2), and mTOR-AKT-STAT5 translational machinery, collectively impairing milk biosynthetic capacity. Pro-apoptotic signaling (BAX, CASP3 upregulation; BCL2 downregulation) and mitochondrial dysfunction further compromise mammary epithelial viability. Post-transcriptional regulation through miRNA, circRNA, and lncRNA competing endogenous RNA networks, alongside epitranscriptomic m6A modifications, adds further regulatory complexity. Genome-wide association studies have identified SNPs in HSP70A1A, HSPA4, TLR4, and PRLR as thermotolerance candidates compatible with sustained milk production. Nutritional supplementation with methionine, arginine, and taurine partially restores cellular synthetic capacity. Integrating multi-trait genomic selection with Bos indicus introgression, precision cooling, and targeted nutrition offers the most viable path toward climate-resilient, high-producing dairy cattle. Full article

Sickle cell disease (SCD) is a monogenic disorder responsible for recurrent vaso-occlusive crises, progressive organ damage, and shortened life expectancy. For decades, allogeneic hematopoietic stem cell transplantation from a matched sibling donor has been the only established cure, but its reach remains limited by donor availability and transplant-related toxicity. The approval of two autologous gene therapy products in 2023, exagamglogene autotemcel (exa-cel) and lovotibeglogene autotemcel (lovo-cel), marked a turning point for the SCD population and the gene therapy field in general. This review proposes a molecular rationale for fetal hemoglobin reactivation and β-globin gene addition, describes the engineering of lentiviral and CRISPR-based platforms, and highlights the clinical evidence accumulated to date that demonstrated durable disease modification with acceptable short-term toxicity. We then assess the clinical positioning of gene therapy within the broader spectrum of curative options compared to current available treatments and address the financial, ethical and psychosocial barriers that limit access to gene therapy both within high-income countries and globally. Critical research priorities include long-term safety surveillance, comparative effectiveness studies, pediatric trials below 12 years, and validated patient-reported outcome instruments. Base editing, non-genotoxic conditioning, and in vivo delivery represent the most promising avenues to broaden access and reduce treatment burden. Full article

Semantic change detection aims to localize changed regions and identify the corresponding land-cover transitions from bi-temporal remote sensing images, which is crucial for applications such as urban expansion analysis, disaster assessment, and environmental monitoring. Although vision foundation models such as the Segment Anything Model 2 provide strong visual priors and powerful feature representations, directly transferring them to semantic change detection remains challenging. In particular, the high-frequency details required for precise boundary delineation are often weakened during feature extraction, while the joint optimization of binary change localization and semantic recognition can introduce task interference. To address these challenges, we present FreqSCD, a SAM2-based framework built on a frozen backbone with three task-specific components: a High–Low-Frequency Adapter for frequency-aware feature adaptation, Task-Decoupled Decoding and Semantic Consistency for reducing task interference, and Local Spatial–Semantic Alignment for improving multi-scale feature aggregation. Experiments on the SECOND and Landsat-SCD benchmarks show that FreqSCD achieves strong semantic change detection performance, obtaining an $F_1$ score of 56.72% and a SeK of 24.17% on SECOND, as well as an $F_1$ score of 85.46% and a SeK of 53.76% on Landsat-SCD. Full article

Among the many complications that can occur in individuals with sickle cell disease (SCD), several studies have suspected an increased risk of cancer. While the effect of SCD on solid tumors remains unclear, multiple studies support a higher incidence of leukemia, especially acute myeloid leukemia (AML). This risk seems to appear in childhood and persist throughout life. Based on these features, should SCD be considered a cancer predisposition syndrome? Here, we explore this question by comparing the characteristics of SCD-associated AML and cancer predisposition syndromes. We show that some features are similar. As in cancer predisposition syndrome, increased cancer risk in SCD appears to be restricted to a defined type of malignancy. SCD-associated AML also has molecular specificities reminiscent of therapy-related AML. Many of the mechanisms contributing to SCD-associated leukemogenesis have been reported in cancer predisposition syndromes, including ineffective erythropoiesis, increased cell renewal, chronic inflammation, and oxidative stress. Nevertheless, SCD presents a unique combination of factors, and their magnitude may greatly vary from one individual to another. Strikingly, the relative risk of cancer in SCD is much lower than most cancer predisposition syndromes and closer to those conferred by common variations. This is a major difference, and indeed, the absolute risk of malignancy in individuals with SCD appears to be low. Moreover, SCD has great clinical variability, and the factors influencing AML risk are unclear. In sum, SCD has many specificities compared to cancer predisposition syndromes that should be considered and investigated. Clinicians should be aware of the increased risk of AML in patients’ management and counseling. Full article

Background/Objectives: Blood transfusion is a crucial component in the treatment of individuals with sickle cell disease [SCD]; nonetheless, multiple transfusions can lead to considerable complications, notably alloimmunization. However, the prevalence of alloimmunization and its predictors remain incompletely explained. This review aimed to determine its global prevalence and identify associated risk factors. Method: Our protocol was registered in PROSPERO [ID: CRD420251167042] in accordance with the PRISMA 2020 criteria. A thorough literature search was conducted across PubMed, Embase, Web of Science, Scopus, and the Cochrane Library to identify studies reporting the prevalence of alloimmunization in adults with confirmed sickle cell disease who have received blood transfusions. This search included all publications up to 16 April 2026. Two reviewers independently screened and extracted data, and the Newcastle–Ottawa Scale was used to evaluate the study’s quality. After the Freeman–Tukey transformation, a random-effects model was used to estimate the pooled prevalence. We examined disparities among groups and geographies, study designs, and matching procedures to determine their differences. We additionally employed meta-regression to identify potential predictors. Results: Nine studies [n = 1711; 1978–2026] met the inclusion criteria. The overall rate of alloimmunization was 28.9% [95% CI 22.4–35.4; I² = 88.5%]. The most prevalent antibodies were those of the Rh and Kell systems, with anti-E antibodies being the most frequent, followed by anti-C and anti-K antibodies. A higher number of transfusions and the HbSβ⁰ genotype were both persistent risk factors, while older age at first transfusion appeared protective. Extended antigen matching dramatically reduced prevalence, though approximately 9% of individuals remained affected. Conclusions: Alloimmunization continues to challenge transfusion management in adults with SCD. Broader implementation of extended antigen matching and genotype-informed transfusion strategies may help mitigate this risk. Full article

Optical and Synthetic Aperture Radar (SAR) sensors capture complementary and consistent information, and their fusion enhances remote sensing image quality. Existing pyramid decomposition-based methods suffer from insufficient texture–edge discrimination. Additionally, the manual setting of parameters during pyramid decomposition introduces uncertainty in the fusion results. To address this problem, we propose an optical and SAR image fusion framework based on local extrema adaptive pyramid decomposition (LEAPFusion), which enhances edge preservation and improves parameter adaptability. Specifically, by leveraging the edge-preserving properties of local extrema, we introduce them into the image pyramid decomposition framework to construct complementary local extrema and Laplacian pyramids. Then, we introduce an explicit parameter adaptation strategy in which the decomposition levels and local extrema kernel sizes are automatically determined from image size and pyramid scale, enabling consistent multi-scale representation and reducing parameter sensitivity compared to empirically tuned settings. Finally, by exploiting the complementary properties of the two pyramids, we implement a multi-type fusion strategy: weighted averaging for low-frequency components and parameter-adaptive pulse-coupled neural network (PAPCNN) for high-frequency details. Our decomposition framework seamlessly integrates three representative edge-preserving filters—a median filter, a guided filter, and a rolling guidance filter—demonstrating strong generalization capability across different filtering paradigms. Extensive experiments on two benchmark datasets demonstrate that our method outperforms seven state-of-the-art algorithms, achieving the best results across diverse scenes with improvements of up to 13.38% in SF and 18.90% in SCD compared to the second-best methods. Full article

The widespread deployment of smart fault recorders (SFRs) in modern power grids faces critical bottlenecks: missing automatic discovery, low modeling efficiency, and incomplete validation coverage. Existing “one-key configuration” schemes and IEC 61850-based platforms still rely on manual intervention for device registration, model mapping, and rule verification, leading to configuration cycles of 2–3 days per substation. This work presents a system-level integration of existing mature techniques into a full-chain automated solution integrating multi-protocol active discovery, layered hierarchical modeling, and four-dimensional service validation. The main improvements include: (1) a link-to-application layer detection mechanism enabling plug-and-play device perception; (2) a dynamic parameter template adaptation algorithm that reduces manual adjustments by 85%; and (3) a four-dimensional rule library covering parameter legality, business logic rationality, cross-device coordination (including relay protection settings), and fault scenario adaptability. In provincial pilot substations, the proposed solution reduces single-device configuration time from 4.5 h to 1.2 h (73.3% improvement), lowers the error rate from 8.2% to 0.8%, and increases validation coverage from ~40% to 96.6%. The solution provides a feasible technical pathway for minute-level deployment and dynamic reconfiguration under flexible grid architectures. Full article

(1) Background and Objectives: Current risk stratification strategies for primary prevention of sudden cardiac death (SCD) have limited sensitivity and specificity. Artificial intelligence (AI) applied to electrocardiograms (ECGs) has emerged as a promising tool to predict the risk of future cardiac arrhythmias. This scoping review synthesizes evidence from original studies evaluating AI models trained on ECGs for risk stratification of SCD/malignant ventricular arrhythmias. (2) Materials and Methods: A comprehensive search of MEDLINE, Embase, Web of Science, Scopus and IEEE Xplore was conducted to identify peer-reviewed studies from inception to February 2026. Eligible studies included original investigations in which the model input was an ECG, recorded at baseline or during monitoring, and the outcome was either short-term or long-term SCD/malignant ventricular arrhythmia risk prediction. Extracted variables included study characteristics, ECG data, AI model data, model performance metrics, and the validation strategy. Risk of bias was assessed using PROBAST. (3) Results: Twenty studies met the inclusion criteria. High-risk cardiovascular subgroups (e.g., heart failure cohort, ICD cohort, etc.) or datasets from admitted patients, and conventional machine learning models or deep learning models were used in most studies. AI-ECG algorithms achieved moderate-to-high discriminative performance for identifying patients at an increased risk for imminent SCD/malignant ventricular arrhythmias (nine studies, AUROC ≈ 0.77–0.96) or future SCD/malignant ventricular arrhythmias (eleven studies, AUROC ≈ 0.66–0.94). However, multiple methodological limitations were identified, including limited sample sizes, susceptibility to overfitting, data imbalance-related bias, heterogeneity in dataset and endpoint definitions, inadequate external validation, and incomplete assessment and reporting of model calibration. (4) Conclusions: AI-ECG models demonstrate potential for risk stratification of SCD and malignant ventricular arrhythmias. However, the current evidence base is constrained by several methodological limitations, and further research is required to determine the clinical utility of AI-ECG for predicting SCD. Full article

Metabolic dysfunction-associated steatotic liver disease (MASLD) and its progressive inflammatory form, metabolic dysfunction-associated steatohepatitis (MASH), are increasingly recognized as key drivers of hepatocellular carcinoma (HCC). Unlike HCC caused by viral infections or alcohol, MASLD/MASH-related liver cancer develops within a chronic immunometabolic environment characterized by lipotoxicity, sterile inflammation, fibrogenesis, and remodeling of the microenvironment. In this setting, interleukin-10 (IL-10) has attracted growing attention due to its complex, context-dependent roles in immune regulation and tumor immune tolerance. This review explores IL-10 biology and its connection to MASLD/MASH-associated HCC, emphasizing the paradox that IL-10 may diminish harmful inflammation in early stages while promoting immunosuppressive conditions in advanced disease. To supplement existing research, we performed an exploratory reanalysis of publicly available bulk liver RNA-seq data from a mouse model that progresses from MASLD/MASH to HCC. The reanalysis revealed a receptor- and effector-specific rewiring of the IL-10 pathway: while the expression of canonical signaling genes (Stat3, Jak1, Jak2, Tyk2, Socs3) showed minimal changes across stages, receptor subunits (Il10ra, Il10rb) and IL-10-responsive effectors (such as Scd2, related to lipid metabolism, and Ddit4, involved in mTOR and glycolysis regulation) displayed strong stage-dependent induction. This was accompanied by a decrease in hepatocyte signature profiles and an increase in stromal and immune signatures. These results generate new hypotheses and raise key questions—particularly whether a large portion of IL-10 modulation originates from peripheral or non-parenchymal sources, and whether the transcriptional patterns observed reflect protein-level changes—that will require stage-specific, cell-focused human studies incorporating proteomic and cytokine measurements. Full article

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a widespread pathology requiring adequate preclinical models for studying pathogenesis and evaluating therapeutic and preventive agents. This study compared differential markers of MASLD pathogenesis in rats using two distinct dietary models: a choline-deficient high-fat diet (HFD-CD) and a cholesterol-enriched high-fat diet (HFD+CHOL). Male Wistar rats were fed either a control AIN93M diet, HFD-CD (40% fat, 20% fructose, and choline deficiency), or HFD+CHOL (40% fat, 20% fructose, and 1% cholesterol) for 56 days. Comprehensive assessment included phenotypic, biochemical, hematological, histomorphological parameters, oxidative stress markers, hepatocyte apoptosis, cytokine levels, and hepatic gene expression. HFD-CD induced steatosis with moderate insulin resistance, increased malondialdehyde levels, and suppressed Acaca, Scd and ChREBP gene expression. In contrast, HFD+CHOL caused macrovesicular steatosis, inflammation, early fibrosis, atherogenic dyslipidemia, intrahepatic cholesterol accumulation, hepatocyte apoptosis, upregulated Srebf1, Cyp7a1, and Nfkb1 expression, and activated Nrf2-dependent antioxidant responses. HFD-CD and HFD+CHOL induce two pathogenetically distinct MASLD phenotypes. The HFD-CD model, characterized by steatosis and oxidative stress without pronounced inflammation or fibrosis, is preferable for studying the preventive potential of bioactive food compounds. Conversely, the HFD+CHOL model with inflammatory and fibrotic components is more suitable for evaluating therapeutic agents aimed at mitigating inflammation, restoring cholesterol homeostasis, and attenuating fibrosis. Full article