Unmanned aircraft are increasingly recognized for their potential to enhance healthcare logistics, offering rapid and reliable transport solutions. Among the many envisioned use cases, emergency medical deliveries stand out as particularly promising due to their immediate societal value. This study investigates the potential of drones operating under U-space to support hospital-to-hospital emergency deliveries in Madrid. Using the GEMMA tool, we modeled and simulated operations with two drone types along direct routes between four hospitals, resulting in six hospital pairs. Drone travel times were estimated and compared against road transport times obtained from the Google Routes API, incorporating one week of traffic data to capture daily and weekend variability. The results show substantial advantages of aerial transport, with time savings ranging from 2 to 26 min, equivalent to 35–58% compared to road transport. Drones consistently ensured deliveries within 15 min, outperforming regular cars (39%) and ambulances or motorcycles in highly congested periods. Sensitivity analysis confirms their reliability in scenarios with strict time constraints, especially under 15 min. These findings demonstrate that drones reduce travel times and improve predictability, providing a robust evidence base for policymakers and regulators to advance U-space integration in healthcare logistics.

The underdiagnosis of Attention-Deficit/Hyperactivity Disorder (ADHD) in females, particularly those with the inattentive presentation, highlights a critical gap in clinical care. While DNA methylation may be tissue-specific, accessible peripheral measures such as cord blood can provide valuable insights into early-life risk and serve as potential biomarkers for detection. We aimed to discover sex-specific epigenetic markers in cord blood associated with childhood ADHD symptoms, which could illuminate early-life risk mechanisms and inform improved detection strategies. Method We conducted sex-stratified epigenome-wide association study (EWAS) of cord blood DNA methylation in relation to ADHD symptoms among child participants (n=2,417; 47% females) of the Norwegian Mother, Father, and Child study (MoBa). We tested for sex-interaction effects and analyzed inattention and hyperactivity/impulsivity symptoms separately. Results We identified 21 differentially methylated CpG positions (DMPs). The majority (n=13) were associated with inattention symptoms exclusively in females, and all exhibited significant sex-interaction effects (corrected p-value <0.05). There was no overlap between the DMPs, or differentially methylated regions (DMRs) identified in females and males, and the epigenetic signatures for inattention and hyperactivity/impulsivity were largely distinct. Several annotated genes (e.g., PNPO, KDM5B, and GABRP) have recognized roles in neurotransmission and neurodevelopment. Conclusion Our findings demonstrate that the peripheral epigenetic profile at birth associated with later ADHD is remarkably different between sexes. This highlights the value of sex-stratified analyses and suggests that peripheral epigenetic markers hold promise for the development of tools for early detection.

Background: This study aimed to systematically review and analyze the available evidence on the safety and efficacy of physical activity (PA) in patients with HCM. Methods: We conducted a systematic search of PubMed, Cochrane, and Web of Science databases up to March 30, 2025. Fourteen studies (4 RCTs) were included in the qualitative synthesis and ten in the quantitative synthesis, totaling 10478 patients. Results: The meta-analysis demonstrated a significant improvement in peak VO2 in the moderate intensity PA (MIPA) group, with a mean difference of 1.77 mL/kg/min (95% CI: 0.93 to 2.60, I2 = 38.2%, p = 0.19), while changes in body mass index were not significant (MD: −0.66 kg/m2; 95% CI: −1.77 to 0.44; I2 = 62%; p = 0.07). No significant differences were observed in the occurrence of non-sustained ventricular tachycardia (NSVT) (OR = 1.54, 95% CI: 0.93 to 2.52, I2 = 28.3%, p = 0.24), atrial fibrillation (OR = 0.89, 95% CI: 0.77 to 1.03, I2 = 28.6%, p = 0.23), or syncope (OR = 1.23, 95% CI: 0.72 to 2.10, I2 = 25.6%, p = 0.24) between the MIPA and sedentary group. Additionally, the occurrence of NSVT between the high-intensity PA and MIPA group showed no significant difference (OR = 1.19, 95% CI: 0.60 to 2.36, I2 = 0%, p = 0.99). Conclusion: The results suggest that regular exercise does not increase the risk of NSVT, AF, or syncope while enhancing peak VO2, indicating that regular exercise is safe and beneficial in HCM patients.

Functional Safety system (software & hardware) development is typically a V-Model process, which is governed by strenuous regulations & norms. This, along with use case specificity, and the scrupulous nature of functional safety creates various bottlenecks across the V-Model, i.e., redundant aspects of functional safety system development. To alleviate these bottlenecks, we introduce two LLM assistants designed to support key V-Model phases. The first assistant, the Digital Safety Assistant (DSA), provides safety engineers with general knowledge of functional safety norms through Retrieval Augmented Generation, thus decreasing norm and application domain adaptation overhead. We benchmark various models and assess the DSA using an official functional safety Certification exam, where the DSA achieves up to 70%, surpassing typical performance levels. A second assistant, the Automated Testing Assistant, developed through Parameter Efficient Fine-tuning to support the V-Model verification phase, is capable of correctly generating and debugging PLC test code with 93% correctness.

The β-catenin destruction complex (BDC) is a central node in WNT/β-catenin signaling, governing embryonic development and adult tissue homeostasis. Although recognized as a prime therapeutic target in colorectal cancer (CRC) for three decades, its dynamic architecture and biochemical complexity have hindered mechanistic understanding. Here, we systematically mapped the sequence-function landscape of the BDC using tiled base editor screens across four endogenous components—CTNNB1, AXIN1, APC, and GSK3B. Validation studies identified ∼150 previously unreported mutations across these genes that affected WNT/β-catenin signaling. In addition to known cancer-associated mutations, we discovered rare gain-of-function and separation-of-function alleles of AXIN1 and CTNNB1 that provide mechanistic insights into complex assembly and regulation. We describe a region in β-catenin that regulates its binding to TCF/LEF transcription factors and demonstrate that the AXIN1–β-catenin interface is critical for controlling signaling flux through the oncogenic BDC. Mechanistic studies revealed that assembly of the oncogenic BDC is scaffolded by its own substrate β-catenin, establishing an autoregulatory mechanism that represents an unexploited vulnerability in cancers harboring common APC truncations. Our comprehensive mutational resource provides a foundation for understanding WNT/β-catenin signaling mechanisms in health and disease, while revealing strategies for therapeutic intervention in WNT-driven cancers.

Extracellular vesicles (EVs) transport biomolecules that could serve as biomarkers for disease diagnosis and monitoring. The clinical utility of EVs derived from cerebrospinal fluid (CSF) in patients with intradural spinal tumors (IST) has not yet been investigated. Here, we obtained EVs from CSF of adult patients with intraspinal ependymoma (n = 9), meningioma (n = 9), hemangioma (n = 4) and schwannian tumors (n = 7), as well as comparison group (‘CG’, normal pressure hydrocephalus, n = 7), by ultrafiltration. CSF-EVs were characterized by electron microscopy and nanoparticle tracking analysis. EV populations according to the presence of tetraspanins (CD9, CD63, CD81) were measured by imaging flow cytometry (IFCM). CD81+ EVs were more prevalent in the comparison group, meningioma, ependymoma WHO grade 2, and hemangioma, whereas CD9+ EVs were predominant in ependymoma grade 1 and Schwannian tumors. CD63+ EVs per milliliter/CSF differed between ependymoma WHO grades 1 and 2 (FC = 24.6, AUC = 90%, p < 0.05). Based on results from a bead-based multiplex profiling, we selected ITGB1, CD44, CD133 and HLA-DR/DQ/DP for further phenotyping in CSF-EVs using IFCM, in combination with each tetraspanin as double-positive subpopulations. Compared to CG, CD44+ EVs were the most relevant population in CSF from IST patients, followed by ITGB1. Notable differences in absolute (EVs/mL CSF) and relative (percentages of CSF-EVs) levels were: CD44+/CD81+ for ependymoma grade 1 (FC = 196.5 and 34.5; p < 0.01) and grade 2 (%FC = 6.1, p < 0.05); CD44+/CD63+ for meningioma (abs. and %FC > 1000, p < 0.05); ITGB1+/CD81+ for hemangioma (%FC = 4.8, p < 0.05); and ITGB1+/CD9+ for schwannian tumors (abs.FC = 19.8, p < 0.01). In conclusion, we identified distinct EV subpopulations in the CSF of IST patients, potentially facilitating tumor classification.

Quercus petraea (Matt.) Liebl. holds significant ecological and economic value, yet phenotypic variation within its natural populations remains only partially explored. The main objectives of the study were: (1) to quantify the phenotypic variation in the analyzed leaf and acorn traits; (2) to determine the proportion of phenotypic variation occurring among and within populations; (3) to examine the pattern of population-level variation and its relationship with bioclimatic factors. Material and methods A total of 13 leaf traits and 10 acorn traits were analyzed in 64 sampled trees from seven natural populations. Descriptive statistics and population differentiation coefficients were calculated. The significance of differences in traits mean among populations and among trees within populations were assessed. Principal component analysis (PCA) was conducted at both the tree and population levels. Correlations between morphological principal components and bioclimatic variables were examined, and regression models were developed to evaluate relationships between significantly correlated morphological traits and bioclimatic factors. Results The coefficients of variation for the 23 traits ranged widely, from 12.08% for cupule width to 36.61% for petiole length, with an average of 20.36%. Nested ANOVA revealed substantial phenotypic variations both within and among populations. The primary source of variation was at the intra-population level, accounting for an average of 68.25%, which exceeded the inter-population variation (30.34%). The analyzed traits were reduced to six principal components, which together explained 88% of the phenotypic variation among trees. A statistically significant correlation between morphological principal components and bioclimatic variables was found for the first component, specifically with temperature related variables. Conclusions The study confirms that Q. petraea in Bosnia and Herzegovina exhibits considerable morphological variability, primarily within populations, and that leaf traits are strongly influenced by climatic gradients. Conservation strategies should prioritize the preservation of population integrity and patterns of local adaptation patterns, particularly in ecologically marginal or historically disturbed sites.

This paper proposes the use of the Linux kernel’s ftrace framework, particularly the function_graph tracer, to generate informative system-level data for machine learning (ML) applications. Experiments on a real-world encryption detection task demonstrate the efficacy of using the proposed features across several learning algorithms. The learner is subjected to the problem of detecting encryption activities across a large dataset of files, where function call traces and graph-based features are used. Empirical results highlight an outstanding accuracy of $99.28 \%$ on the task at hand, underscoring the efficacy of features derived from the function_graph tracer. The results were further validated using an additional experiment targeting a multi-label classification problem by identifying the running programs based on trace data. This work provides comprehensive methodologies for preprocessing raw trace data and extracting graph-based features, offering significant advancements in applying ML to system behavior analysis, program identification, and anomaly detection. By bridging the gap between system tracing and ML, this paper paves the way for innovative solutions in performance monitoring and security analytics.

We consider a large-scale data center where a fleet of heterogeneous mobile robots and human workers collaborate to handle various installation and maintenance tasks. We focus on the underlying multi-agent task assignment problem which is crucial to optimize the overall system. We formalize the problem as a Markov Decision Process and propose an end-to-end learning approach to solve it. We demonstrate the effectiveness of our approach in simulation with realistic data and in the presence of uncertainty.

ObjectiveTo evaluate the predictive value of LA strain parameters and LASI for AF recurrence following electrical CV, and to compare them to conventional echocardiographic, biochemical, and clinical markers.MethodsIn this prospective, observational pilot study, 31 patients with persistent AF underwent electrical CV and were followed for six months. Echocardiographic evaluation included LA reservoir, conduit, and contractile strain, left atrial stiffness index, left atrial volume index (LAVI), left atrial appendage (LAA) morphology, left ventricular ejection fraction (LVEF), right atrial (RA) area, and right ventricular systolic pressure (RVSP). AF recurrence was assessed at three and six months.ResultsAt three months post-CV, LA reservoir, conduit, and contractile strain values were significantly negatively associated with AF recurrence (p < 0.001), while LASI and E/E' ratios were positively associated (p < 0.001). At six months, only contractile strain retained prognostic significance (p = 0.008). LVEF showed a positive correlation with recurrence at six months (p = 0.003), potentially reflecting the role of diastolic dysfunction.ConclusionLA strain parameters and LASI are valuable tools for predicting AF recurrence after CV, particularly in the early post-procedural period. Contractile strain may serve as a more reliable long-term predictor, emphasizing the importance of longitudinal atrial function assessment in rhythm outcome prediction. However, given the small sample size and single-center design, these results should be considered hypothesis-generating, requiring validation in larger studies.

Aim: This manuscript summarizes the key scientific and practical outcomes of the #DHPSP2024 digital networking event, focusing on emerging trends in digital health technologies, innovations in patient safety, and their implications for improving healthcare delivery. Methods: The #DHPSP2024 event was held from June 18 to 20, 2024, on X (formerly Twitter) and LinkedIn, connecting professionals and stakeholders in digital health and patient safety from different sectors. Data from posts on X and LinkedIn were analyzed for geographical distribution, engagement metrics (impressions, likes, shares), top hashtags, and frequently used terms. A qualitative analysis of the central themes and key online messaging discussions of the network event was also conducted. Results: On X, 2,329 posts by 179 participants from 38 countries generated over 231,000 impressions, with the most activity in Austria, China, and India. LinkedIn engagement included 3,475 likes, 217 comments, and 2,030 shares. Both platforms highlighted core themes such as digital health, patient safety, treatment quality, research on natural compounds, and interdisciplinary collaboration. Online messaging discussions emphasized technologies like telemedicine and artificial intelligence as critical tools for enhancing care delivery and patient safety. Participants also promoted special issues of scientific journals and explored collaborative research opportunities. Conclusions: The #DHPSP2024 event underscored the pivotal role of digital technologies in transforming healthcare, particularly in improving the quality and safety of interventions. The findings demonstrate how digital networking events, grounded in open innovation, foster global research communities, accelerate knowledge exchange, and support the integration of clinically relevant digital solutions. The strong engagement reflects growing interest in leveraging digital platforms to advance health outcomes and professional development. Overall, the event contributed to greater visibility of ongoing research, encouraged interdisciplinary cooperation, and may positively influence both the adoption of innovations in healthcare practice and the dissemination of scientific knowledge.

Research on mental health has traditionally separated the study of ill-being, including clinically defined mental and behavioural disorders and subthreshold problems, from the study of well-being, which encompasses factors such as life satisfaction and positive affect. Although previous reviews of studies primarily using self-report scales indicate that ill-being and well-being are distinct yet interconnected constructs, a deeper examination of their relationship is lacking. In this Perspective, we synthesize genetic, biological, developmental, psychosocial, societal, cultural and clinical research on ill-being and well-being. Our review reveals substantial genetic overlap and similar biological underpinnings for ill-being and well-being. By contrast, environmental factors and societal changes often exert divergent influences. We propose a differentiated multidisciplinary framework in which the shared and unique determinants, predictors, mechanisms and consequences of mental ill-being and well-being vary across levels of analysis, offering a more nuanced understanding of the interconnections. Tamnes et al. explore the complex relationship between ill-being—including mental and behavioural disorders—and mental well-being.

Energy security is currently one of the most important topics worldwide. Maintaining a reliable energy supply is one of the biggest challenges in security science. Additionally, defending energy infrastructure from cyberattacks is an ongoing issue. Understanding the vulnerabilities of energy infrastructure, especially the Smart Grid, which relies on information technology and communications, is a significant advantage. Understanding which system vulnerabilities lead to specific cyber threats presents a significant opportunity, enhancing the defence of energy infrastructure. This paper uses a systematic literature review to identify the most common cyber threat and Smart Grid vulnerability mentioned and researched in the literature from 2018 to 2025. This paper also aims to map the vulnerabilities that allow for cyber threats to occur, with the idea that if we know what causes a weak spot, we can effectively prevent it. Identifying specific weaknesses that could lead to cyber threats allows us to mitigate these dangers by addressing and correcting those vulnerabilities.