Search Results (2,089)

Heart failure (HF) is a global health challenge characterized by the heart’s inability to satisfy metabolic demands, driven by renin–angiotensin–aldosterone system (RAAS) overactivation, a neurohormonal imbalance, and emerging mechanisms like the gut–heart axis and mitochondrial dysfunction. Affecting over 6 million adults in the US alone, HF incurs a 5-year mortality rate of 50% and escalating costs projected to double by 2030. This review examines HF’s molecular paradigms, integrating established pathways with advances in omics, stem cell therapy, genetic modification, and personalized medicine. The RAAS blockade remains central, yet its efficacy is limited in HF with preserved ejection fraction (HFpEF). Stem cell therapies (mesenchymal and induced pluripotent stem cells) show regenerative potential but face poor retention (<10% survival at 30 days). CRISPR/Cas9 offers precision, though off-target effects persist. The gut microbiome, via trimethylamine N-oxide, exacerbates inflammation, while omics technologies promise biomarkers for tailored treatments. Challenges include translating these innovations into practice, particularly for HFpEF. Future directions involve novel HFpEF therapies, enhanced stem cell delivery, precise genetic tools, and microbiome interventions, supported with artificial intelligence. By 2030, these advances could shift HF management toward regeneration, contingent on overcoming translational barriers through global collaboration. Full article

Recent advances in single-cell RNA sequencing (scRNA-seq) have substantially deepened our understanding of adult hippocampal neurogenesis (AHN), enabling the detection of neural stem cells, progenitors, and immature neurons in postmortem human brain tissue and revealing how these populations are altered in neurological disease. Additionally, scRNA-seq enables the identification of disease-specific cell subtypes and distinct gene expression signatures associated with neurological disorders, many of which are linked to alterations in AHN and cognitive function. Such cellular- and molecular-level insights into neurological disease mechanisms provide a strong foundation for the development of targeted therapeutic strategies. Indeed, scRNA-seq has also emerged as a powerful tool in drug discovery and development across multiple disease areas, including cancer, cardiovascular disorders, and neurological conditions. In this review, we offer a comprehensive and integrative perspective on the cellular and molecular landscape of human hippocampal neurogenesis, the pathological mechanisms underlying neurological disorders, and their implications for therapeutic development. Full article

Introduction: Intensive chemotherapy protocols and hematopoietic stem cell transplantation (HSCT) in children with cancer frequently lead to severe complications, such as mucositis and immune dysfunction. A growing body of evidence indicates that these complications are closely associated with the patient’s nutritional status and the composition of the gut microbiome, which becomes profoundly destabilized as a result of cytotoxic therapy and antibiotic use. Background: The aim of this review is to critically evaluate the current state of knowledge on the interplay between gut dysbiosis, metabolomic profiles—with particular emphasis on short-chain fatty acids (SCFAs)—and treatment-related toxicity in pediatric patients, as well as to delineate pathways toward personalized nutritional therapy. Methods: A narrative review was conducted, including clinical and preclinical studies published between January 2015 and October 2025. PubMed/MEDLINE, Embase, Cochrane Library, and other databases were searched, focusing on changes in microbiome composition, correlations between gut-derived metabolites and the severity of complications (sepsis, graft-versus-host disease [GvHD], mucositis), and the effects of targeted nutritional interventions (probiotics, prebiotics, postbiotics, and fecal microbiota transplantation [FMT]) on microbiome modulation during anticancer therapy. Results: The analysis demonstrates that pediatric oncologic treatment leads to a marked reduction in microbial diversity, including the loss of protective Clostridiales taxa (e.g., Faecalibacterium), accompanied by an overgrowth of Proteobacteria pathobionts. Metabolomic profiling indicates that low SCFA levels (e.g., butyrate < 20–50 µmol/g) are a strong predictor of severe mucositis, prolonged neutropenia, and an increased risk of sepsis. Interventions aimed at restoring eubiosis and enhancing SCFA production show potential in strengthening the intestinal barrier, modulating immune responses, and enabling maintenance of the planned relative dose intensity (RDI) of chemotherapy by reducing treatment-related toxicity. Conclusions: Gut microbiome profiling and fecal metabolomics represent promising prognostic tools in pediatric oncology. There is an urgent need for further research employing “omics”-based approaches to develop precise, individually tailored nutritional protocols. Such strategies, including postbiotics and FMT, may minimize treatment-related adverse effects and improve long-term clinical outcomes in pediatric patients. Full article

Despite the improvements in tool development for DNA methylation analysis, there is a lack of a consensus on computational and statistical models used for differentially methylated cytosine (DMC) identification. This variability complicates the interpretation of findings and raises concerns about the reproducibility and biological significance of the detected results. In this regard, here we conducted a comparative evaluation of edgeR and methylKit tools to assess their performance, concordance, and biological relevance in detecting DMCs following a morphine exposure model in mouse embryonic stem cells (mESCs). Both pipelines were applied to the same WGBS dataset (GEO accession number: GSE292082), and concordance was calculated at both single-base and gene levels. Although the total number of DMCs identified differed between tools, both pipelines detected a global hypomethylation pattern. Genomic distribution analysis revealed that DMCs predominantly localized to intergenic and intronic regions, as well as to open sea regions. Despite differences in sensitivity, both pipelines demonstrated moderate concordance at the DMC level (~56%) and high concordance at the gene level (~90%), identifying largely overlapping sets of differentially methylated genes (DMGs). Comparative assessments further showed that the choice of statistical metric can influence the perceived magnitude of biological effects. Sensitivity analyses indicated that threshold selection and normalization methods influence DMC detection, whereas aggregation at gene level reduces discrepancies. Overall, our findings underscore the complementary strengths of methylKit and edgeR and highlight the importance of careful tool selection for epigenetic studies. As a conclusion, we recommend integrating both pipelines to ensure a balanced interpretation of effect sizes, particularly in studies with complex experimental designs. Full article

Out-of-school-time (OST) programs in the United States offer significant opportunities for youth to engage with and develop their identities in science, technology, engineering, and mathematics (STEM). However, professional learning that supports OST educators in developing identity-affirming STEM facilitation remains chronically underfunded and undervalued. Dominant approaches to measuring program “costs”—often centered on per-participant expenditures or short-term cost-effectiveness—obscure the systemic, relational, and capacity-building investments required to sustain high-quality OST STEM practices. This commentary examines how available cost frameworks shape what is rendered visible as “value” in OST STEM professional learning and where they fall short. To ground this analysis, I draw on the Afterschool Coaching for Reflective Educators in STEM (ACRES) program, a long-running national coaching initiative, as an illustrative case through which to examine how investments unfold over time and across contexts. Using ACRES, I demonstrate how costs are more productively understood as multidimensional investments in infrastructure, human capacity, relationships, and knowledge—forms of value that resist per-participant or short-horizon accounting. I offer an alternative tool, the Capacity-Based Cost Assessment (CBCA), to facilitate reflection on the outcomes of these investments. I include recommendations for how to define, document, and evaluate investments in OST STEM professional learning. Full article

Educational drones have become increasingly important in education and research due to their affordability, user-friendly design and control, and potential use as tools in STEM (Science, Technology, Engineering, and Math) learning. For example, CoDrone EDUs are used to teach basic programming principles and drone control to high school or university students. As drones in real-world applications often collaborate to solve problems, controlling multiple educational drones in a team is crucial and beneficial for enhancing students’ problem-solving and design skills. However, these educational drones primarily rely on one-to-one control via a radio-frequency remote controller, and programming libraries for coordinating multi-drone missions are limited, posing challenges for students or developers in controlling them effectively. To address the lack of control in missions with multiple educational drones, we present a drone control station (DCS), featuring a centralized architecture that connects and controls various drones. We first develop scenarios and use cases that utilize multiple drones, specifying the system requirements. We then design conceptual models and architectures for the DCS. Next, we implement the DCS and evaluate whether it achieves the team missions. Experimental results show that the DCS with the centralized architecture is suitable for team missions with multiple educational drones. We expect the approach in our work to serve as a method for controlling multi-drone missions in an educational environment. Full article

Background: Mesenchymal stem cells (MSCs) exhibit a high capacity for differentiation, possess anti-inflammatory and proangiogenic properties, and stimulate the growth and proliferation of neighboring cells. MSCs are a promising tool in regenerative medicine. However, the molecular mechanisms underlying the properties of these cells are not yet fully understood. Gene expression in MSCs influences their characteristics and differentiation potential. Therefore, it is essential to investigate factors affecting gene expression as well as those activating signaling pathways, which will enable more effective and individualized applications of MSCs. In this study, we aimed to identify signaling pathways involved in gene expression in umbilical cord-derived MSCs (UC-MSCs) that may be altered by maternal diabetes and hypothyroidism during pregnancy. Methods: The research material consisted of UC-MSCs. Samples obtained from nine participants were analyzed. UC-MSCs were isolated and cultured, and RNA was extracted. The isolated RNA was used for microarray-based gene expression analysis. Subsequently, pathway enrichment analysis was performed to identify the signaling pathways involved. Results: In the diabetes group, 340 genes (0.71%) were upregulated, while 268 genes (0.56%) were downregulated compared with UC-MSCs from the control group. In the diabetes group, the most compact module was composed of proteins associated with WNT/planar cell polarity (WNT/PCP) signaling. The second module included genes related to smooth muscle activity. In the hypothyroidism group, an association was identified between the extracellular matrix organization pathways (GO:0030198) and the extracellular structure organization (GO:0043062) pathways. Moreover, in this group, increased expression of MMP1, MMP10, and GREM1 was observed. Conclusions: In summary, our study demonstrated the impact of diabetes and hypothyroidism on gene expression in UC-MSCs. We also observed the activation of distinct signaling pathways depending on the presence of these conditions. However, this work represents a preliminary screening, and the results should be validated by PCR in a larger cohort. Full article

The latest advances in remote sensing play a central role in providing Earth observation (EO) data for numerous applications in the scope of reaching environmentally sustainable goals. However, over tropical rainforests, optical imaging is often hindered by extensive cloud coverage, which means that analysis-ready images are mostly restricted to the dry season. In this study, we propose combining radar features extracted from short time series of Sentinel-1 Interferometric Wide Swath (IW) data with a deep learning-based classification scheme to continuously monitor the state of forests. The proposed methodology is based on the joint use of SAR backscatter and interferometric coherences at different temporal baselines to perform pixel-wise classification of land cover classes of interest. However, we show that for a sequence of Sentinel-1 time series, different land cover classes exhibit particular seasonal-dependent variations. Another challenge in performing short-term predictions stems from the fact that ground truths are usually available only on a yearly basis. To address these challenges, we propose a seasonal sampling of the training data, masked by potential deforestation, along with a classification based on a modified U-Net model. The classification results show that overall accuracies above 90% can be achieved throughout the whole year with the proposed method, emerging as a potential tool for mapping rainforests with unprecedented temporal resolution. Full article

The S-100 standard for Electronic Chart Display and Information Systems (ECDIS) uses Lua scripts to render electronic charts, yet lacks security specifications for script execution. This paper evaluates automated Static Application Security Testing (SAST) tools versus expert manual review for S-100-compliant software. Four SAST tools were applied alongside an expert review of OpenS100, a reference implementation for next-generation ECDIS. While automated tools identified numerous defects, they failed to detect 83% (19/23) of expert-identified vulnerabilities, including an unrestricted Lua interpreter flaw with a Common Vulnerability Scoring System (CVSS) score of 9.3. This vulnerability enables Remote Code Execution (RCE) via malicious portrayal catalogues, verified through Proof of Concept (PoC) development. The analysis demonstrates that SAST tools are constrained by limited maritime domain knowledge and challenges in analyzing cross-language semantic risks at the C++–Lua interface. The findings establish that identified vulnerabilities stem from specification gaps in the S-100 standard rather than isolated coding errors. These results indicate that functional safety certifications require supplementation to address design-level security risks. The evidence supports that the International Hydrographic Organization (IHO) incorporate security controls, such as script sandboxing and library restrictions, into the S-100 framework before the 2029 mandatory adoption deadline. Full article

Background and Objectives: The regeneration of the pulp–dentin complex represents an alternative to conventional root canal treatment, aiming to preserve tooth biology and function. Cell-free regenerative endodontic therapy (CF-RET) exploits endogenous stem cells from the periapical region without ex vivo cell manipulation. Despite growing interest, the biological mechanisms, clinical indications, and predictability of CF-RET remain not clearly defined. This structured narrative review aimed to update a previous review by analyzing recent human studies on CF-RET. Materials and Methods: This review was conducted using the PRISMA 2020 guidelines to guide transparent reporting of the literature search and study selection process and was registered in PROSPERO (CRD420251075131). In vitro and in vivo human studies published between January 2017 and December 2024 investigating CF-RET were included, while studies involving cell transplantation, non-human models, case reports, and reviews were excluded. Study selection, data extraction, and quality assessment using the QuADS tool were performed, and the evidence was synthesized using a qualitative narrative approach. Results: Sixty-four studies were included. In vitro studies reported favorable effects of growth factors, exosomes, and biomimetic scaffolds on stem cell viability, migration, proliferation, odontogenic differentiation, and angiogenesis, while neurogenic differentiation was less consistently investigated. Scaffold composition, microstructure, and rheological properties were also considered. In vivo studies mainly focused on immature teeth with incomplete root development and demonstrated positive clinical and radiographic outcomes, including root development and canal diameter reduction. Conclusions: The current evidence supports the biological potential of CF-RET as a regenerative approach; however, substantial heterogeneity, the limited number of clinical studies and the absence of standardized protocols preclude definitive conclusions, highlighting the need for further well-designed translational and clinical investigations considering clinical applicability. Full article

Acute and chronic kidney diseases remain significant challenges in regenerative medicine, with few therapies capable of reversing tissue injury or preventing progression. Bone marrow mesenchymal stem cell-derived exosomes (BM-MSC-Exos) are nanosized vesicles (30–150 nm) that have emerged as multifunctional nanotheranostic platforms, combining targeted therapeutic activity with imaging-enabled monitoring. In renal pathophysiology, BM-MSC-Exos exert anti-inflammatory, anti-fibrotic, angiogenic, and pro-regenerative effects. These actions are mediated by microRNAs, messenger RNAs, mitochondrial regulators, and bioactive proteins that modulate epithelial repair and immune responses. Recent bioengineering advances enable more precise BM-MSC-Exos design, including enrichment with synthetic RNAs or gene-editing components and membrane functionalization to enhance kidney tropism. In parallel, fluorescence, bioluminescence, and nanoparticle-based approaches support in vivo tracking. These tools allow real-time assessment of biodistribution and tubular uptake, strengthening evidence for target engagement. This review synthesizes current knowledge on BM-MSC-Exos in renal repair. We summarize contemporary strategies for cargo and surface engineering, outline imaging methodologies for in vivo tracking, and discuss how administration routes influence renal targeting. We also provide an updated overview of clinical trials evaluating exosomes as therapeutic agents or biomarkers in nephrology. Collectively, engineered BM-MSC-Exos represent a promising and increasingly sophisticated platform for precision-guided kidney therapy, supported by monitoring tools that facilitate preclinical evaluation of biodistribution and efficacy. Full article

Background: Although traditional rehabilitation methods are effective in promoting recovery for patients with disabilities, some approaches can involve repetitive tasks, making it challenging to maintain high patient engagement and adherence. This can impact the amount of therapy patients receive, which can sometimes limit their overall recovery potential, particularly given constraints in healthcare resources. Extended reality (XR) technologies, which include virtual reality (VR) and augmented reality (AR), offer promising benefits to personalize care and enhance rehabilitation and engagement by increasing motivation and engagement through interactive and immersive environments. Despite these promising advantages, their successful integration in clinical practice has remained limited, partly due to lack of early involvement of clinicians and end-users in the development process. Objective: We aim to provide recommendations for XR rehabilitation technology development, including researchers and industry professionals, to foster more personalized, adoptable and effective tools for patients with neuromusculoskeletal disorders in a clinical setting. Methods: Principles from motor control and game theory are used to describe key features and recommendations for XR rehabilitation technology development to optimize rehabilitation applications in a clinical setting. These recommendations stem from established motor learning and game design principles, a state-of-the-art narrative review of emerging XR rehabilitation literature (2015–2025) and insights from the Ensemble! Program, a living lab where clinicians, researchers, and patients collaborate to explore emerging technologies, including but not limited to serious games using XR technologies. Results: Key design recommendations include strategies for supporting patient motivation, adjusting game difficulty, providing feedback and handling data collection. Conclusions: Integrating motor control and game theory principles into XR rehabilitation technology can help optimize its therapeutic effectiveness and clinical applicability for patients with neuromusculoskeletal conditions. By addressing clinician and patient needs early in the development process, these technologies can be better tailored to meet therapeutic goals and facilitate broader adoption in clinical practice. Full article

Against the backdrop of the synergistic advancement of Industry 4.0 and the dual-carbon strategy, traditional university mathematics education struggles to meet the demands for cultivating engineering talents’ integrated competencies in mathematics, specialization, and application. The STEM education paradigm urgently needs innovation. Guided by sustainable development principles, this study explores integrated approaches to university mathematics teaching for advanced manufacturing. It constructs a four-stage cyclical framework, Concept–Algorithm–Equipment–Evaluation (CAEE), and integrates Fourier Transform systems into industrial inspection workflows, using silicon carbide wafer thickness measurement as a case study. Targeting second-year students in Measurement and Control Technology and Instrumentation, a comparative design involving an experimental and a control group was employed. Comprehensive evaluation utilized AI-powered dynamic questionnaires, multimodal eye-tracking and EEG data, along with mixed-methods research. Results indicate that the assessment tools achieved high reliability and validity (0.906). The experimental group demonstrated significantly superior performance in deep learning proficiency and subject-specific educational structure (effect size 0.67) compared to the control group, along with modest positive enhancements in cognitive engagement and social interaction dimensions. This pedagogical model transcends conventional ‘knowledge collage’ integration, transforming mathematics from an external auxiliary tool into an ‘endogenous variable’ within industrial systems. It establishes a replicable and scalable STEM education practice paradigm. Full article

This paper introduces LeapNP (Learning and Planning Framework for Numeric Problems), a lightweight, Python-native framework engineered to support both classical and numeric planning tasks. Designed with a fully modular interface, it specifically aims to facilitate the seamless integration of deep learning methodologies. The design philosophy of LeapNP stems from the observation that traditional planners, while highly efficient, lack the necessary flexibility for experimental research, particularly at the intersection of learning and planning. Most state-of-the-art engines are built as highly optimized, rigid executables that are resistant to internal modification. LeapNP disrupts this paradigm by offering a framework where the entire planning stack is accessible and mutable. Users can seamlessly plug in custom implementations for grounding, define novel state representations, or design bespoke search strategies, thereby enabling a level of integration with learning models that is currently impractical with standard tools. By significantly lowering the engineering barrier, our planner fosters rapid experimentation and accelerates research in neuro-symbolic planning. We also present a comprehensive suite of search algorithms, designed to evaluate different properties of learned heuristics. These include two algorithms designed to exploit batching to maximize inference throughput, and a greedy algorithm meant to test the intrinsic robustness of the learned models, running them as general policies. Full article

Polygon Boolean operations are widely used in integrated circuit (IC) layout processing tasks such as design rule checking (DRC) and optical proximity correction (OPC). Single-threaded Boolean algorithms cannot meet the efficiency demand of modern IC layouts, necessitating parallel algorithms for acceleration. However, existing parallel algorithms exhibit unsatisfactory parallel speedups and limited scalability, which typically stem from an inefficient merging phase that uses generic Boolean OR operations and redundantly reprocesses all edges of polygons on grid boundaries. To solve these problems, we proposed Polygon Tailor, a novel parallel algorithm for polygon Boolean operations that employs a data-parallel strategy and a new merging approach performing incremental XOR operations solely on edges along grid boundaries, eliminating redundant computations in previous methods. This innovation drastically reduces the grid-merging time by 1–2 orders of magnitude. Compared with the parallel implementation from a commercial layout processing tool, PolygonTailor is on average 5.08× faster and up to 14.36× faster for OR operations that generate highly complex polygons. Full article