Search Results (83,389)

Acinetobacter baumannii is a critical priority ESKAPE pathogen whose resistome co-occurrence architecture, temporal dynamics, and One Health distribution remain poorly characterized. A total of 20,739 genomes (2000–2025; NCBI, PubMLST, BV-BRC) were annotated using CARD-RGI v6.0.5, AMRFinderPlus v4.2.7, and ResFinder v4.7.2. A co-occurrence network was constructed using Jaccard filtering (≥10%; >0.30) with Bayesian bootstrap and Louvain. Temporal trends were assessed by linear regression with Benjamini–Hochberg (2003–2024), and One Health compartments were assessed using balanced PERMANOVA (Bray–Curtis, 999 permutations). The network comprised 41 nodes and 82 edges with a small-world topology; five communities and nine hub genes were identified. Eighteen ARGs showed significant trends (FDR); six last-resort determinants (blaOXA-23-like, blaNDM, armA, ftsI, msr(E), mph(E)) increased in prevalence. The compartment explained significant variance in the resistome (R² = 0.210; p < 0.001). abaF showed the highest One Health convergence (0.814), and blaNDM was detected in 52 countries. The A. baumannii resistome exhibits a convergent architecture of co-resistance: a small-world network preserved in One Health compartments for more than two decades. The rise in six last-resort determinants signals a population-based transition toward broader profiles, establishing a framework for integrated antimicrobial resistance surveillance. Full article

Background/objectives: Surveillance of local antibiotic resistance plays a critical role in guiding empirical antibiotic therapies in emergency departments (EDs). This study aimed to collect and analyze a five-year period of prevalence and AMR rates among “ESKAPE” pathogens in a tertiary-care ED setting. Methods: This retrospective observational study included a complete census of microbiological specimens collected from patients presenting to the emergency department between 1 January 2019 and 31 December 2023. Data was retrieved from the MedBakter laboratory information system. Non relevant isolated and duplications were excluded. Only the first isolates per patient per phenotype were included in the final dataset. Results: The final dataset contained 6510 isolates. The most frequent isolates were E. coli (2717, 41.7%), K. pneumoniae (715, 11.0%), and P. mirabilis (696, 10.7%) followed by E. faecalis (662, 10.2%). Prevalence of multi-drug resistance (MDR) was 23.26% among “ESKAPE” pathogens, with highest prevalence in M. morganii (79.6%), P. stuartii (78.5%) and S. marcescens (77.1%). Difficult-to-treat resistance (DTR) was found in 32 isolates, while extensive drug resistance (XDR) was found in 5 isolates. The prevalence of other high-priority MDR bacteria was 12.4% for MRSA and 4.7% for VRE, while among Gram-negative bacteria the prevalence of carbapenem-resistant Enterobacterales was 0.52%. The prevalence of cephalosporin-resistant Enterobacterales varied greatly between 0 and 33%. Conclusions: Despite these important AMR trends, the cumulative antibiogram, according to the World Health Organization (WHO)-issued Access, Watch and Reserve (AWaRe) classification, revealed room for empirical antibiotic choices that spare Watch and Reserve agents. The results will aid the revision of the local antibiotic guidelines. Full article

Background: Recent advancements in enabling technologies, including artificial intelligence and telemedicine, alongside robust clinical study outcomes, have led to significant progress in upper limb and hand exoskeletons utilised for post-stroke rehabilitation. Objectives: This review aims to synthesize the recent scientific literature (2010–2025) on post-stroke upper-limb and hand exoskeletons, with particular attention to the sensing architectures—sensing modalities, signal processing, sensor fusion, and sensor-driven control—that integrate technical and biomedical domains to examine device architecture, clinical context, and outcome selection. Methods: A search of PubMed and Scopus was conducted on 10 November 2025, cross-checked against IEEE Xplore, Web of Science, Embase, and ACM Digital Library. We included studies evaluating wearable exoskeletons or robotic orthoses for the upper limb/hand in post-stroke rehabilitation. Two independent reviewers screened records and extracted data, with disagreements resolved by consensus. Data were synthesised using a predefined label-based taxonomy. The review protocol was not registered. Results: From 1889 identified records, 219 studies met the inclusion criteria. The synthesis reveals a transition from rigid, laboratory-centered systems to lighter, soft, and home-oriented solutions. Available evidence suggests potential impairment-level benefits, particularly for proximal motor control, but certainty remains limited due to heterogeneity, small samples, blinding limitations, inconsistent dosing, and limited long-term follow-up; gains in hand/finger dexterity appear even more variable. Discussion: While exoskeleton-assisted therapy appears associated with impairment-level gains, transfer to activities of daily living (ADLs) and real-world function remains inconsistently documented and insufficiently powered to support firm conclusions. Full article

This paper develops a process ontology of spacetime grounded in the primacy of motion. Standard interpretations of relativity are commonly understood in terms of a four-dimensional manifold of events, with motion represented by world-lines connecting temporally ordered event-points. Although mathematically and empirically successful, this framework encourages an event-based metaphysics in which motion is derivative and temporal becoming plays no fundamental ontological role. Full article

Various explanatory factors for perceived happiness have been identified in cross-country comparisons, but their relative importance and causal relationships remain unknown. Associations and causality were studied between 17 societal, economic, and religious variables contributing to happiness scores on the World Happiness Report (WHR) in 147 countries. In a correlation analysis, the strongest association with national happiness scores was observed for the median gross domestic product (GDP)/capita (r squared 0.69), followed by the mean GDP/capita (0.65), tertiary education (0.42), corruption (−0.41), democracy index (0.35), religious non-affiliation (0.31), and consanguinity (−0.25). Bayesian statistical analysis indicated strong causal cascades originating from democracy (a positive effect) and, to a lesser degree, corruption (a negative effect) during the years 1945–1985 to subsequent national happiness levels, with a delay of several decades. The results were confirmed to have good reproducibility with Bootstrap resampling, indicating robust findings. Although the median GDP/capita had the strongest association with happiness in the cross-sectional analysis, it was not identified as a primary causal factor in the Bayesian analysis, in which high levels of democracy and low levels of corruption were entangled with median GDP/capita as apparent prerequisites for perceived happiness. Our results suggest that the median GDP should be used as an indicator of the economic prosperity of citizens instead of the currently established mean GDP when studying the links between economy, democracy, and well-being across countries. Full article

The analysis of circular data with discrete models has been a challenge. To address this, we introduce the wrapped discrete linear exponential (WDLE) distribution, which is a new model obtained by wrapping the existing discrete linear exponential distribution around the unit circle. Notably, the probability mass function of the WDLE model can be stated as a mixture of a wrapped geometric distribution and a wrapped discrete gamma distribution, making it a simpler and more understandable model for dealing with circular data. This model includes closed-form formulas for essential distributional properties such as the characteristic function, trigonometric moments, and circular measures of location and dispersion, making it suitable for use in a variety of practical applications. Maximum likelihood is used for parameter estimation, and simulation studies are used to assess performance. The effectiveness of the model for circular data analysis was confirmed by applying it to two real-world datasets, where it demonstrated higher performance under various information criteria. Full article

Beyond 5G and future 6G services require radio access networks to support heterogeneous applications with diverse latency, reliability, throughput, mobility, and computing requirements. These challenges are particularly pronounced in vehicle-to-network (V2N) communications because of high mobility, dynamic channel conditions, frequent handovers, and heterogeneous service requirements. Conventional traffic-steering methods primarily rely on radio-side indicators, while computing-resource availability and traffic-specific computation demands are often considered separately. To address this limitation, this paper proposes a coordinated communication and computing resource management framework for O-RAN-based V2N communications. The framework integrates a traffic-management rApp (TM-rApp) in the non-real-time RIC with a traffic-steering xApp (TS-xApp) in the near-real-time RIC to enable policy-based closed-loop control. Candidate cells are ranked using communication quality, computing-resource capability and availability, predicted throughput, mobility characteristics, and traffic-class priority. As a proof-of-concept supporting component, proactive throughput forecasting is evaluated using standalone LSTM and stacked ensemble (S-LSTM) models based on lagged radio, mobility, load, and throughput features. The S-LSTM provides an adaptive mechanism for combining base learners but does not achieve a statistically significant improvement over the standalone LSTM; moreover, the forecasting evaluation uses fixed, non-optimized hyperparameters and a single chronological train–test split without cross-validation. Accordingly, the prediction results are interpreted as preliminary evidence of forecasting feasibility rather than as a definitive predictive-performance contribution. The framework further incorporates O-RAN-compatible traffic-steering policies, a minimum dwell-time constraint, and priority-aware resource allocation. Evaluation using a real-world corridor based on Al Haramain Expressway Road in Jeddah and a synthetic straight-highway scenario shows that the proposed method improves SLA compliance over RSS and HHAARC, achieves the highest computing-resource satisfaction, and reduces handovers relative to RSS. The results demonstrate a balanced trade-off among SLA compliance, computing-resource satisfaction, delay, throughput, and mobility robustness, while also showing that load-aware steering can provide higher aggregate SLA compliance under specific traffic distributions. Full article

Background: Regenerative supply chain research seeks to move beyond minimal-harm sustainability toward forms of organizing that preserve, restore, and enhance social–ecological systems. However, existing work often explains regeneration through principles, capabilities, and governance arrangements while giving less attention to the uneven socioecological conditions and appropriative dependencies that shape supply-chain activity. Methods: This conceptual study uses an abductive close reading of Anna Lowenhaupt Tsing’s The Mushroom at the End of the World as a sole-source textual dataset. The analysis combines an immanent reading of the complete monograph with a theory-informed reading that places Tsing’s concepts in dialogue with regenerative supply-chain scholarship. Results: The study develops two linked theoretical shifts. First, it conceptualizes supply chains as patchy socioecological assemblages composed of firms, livelihoods, infrastructures, ecological processes, and disturbance histories that only partially cohere under managerial control. Second, it defines regenerative attunement as the ongoing, place-sensitive reconfiguration of supply-chain scale, timing, governance, and value distribution so that economic activity helps reproduce rather than merely appropriate socioecological capacities. Conclusions: These concepts extend regenerative supply-chain theory by clarifying the relationships among supply-chain structure, value appropriation, temporal plurality, and distributed governance, while providing directions for managerial diagnosis and future empirical research. Full article

Conventional diagnosis of chronic kidney disease (CKD) relies predominantly on invasive blood-based examinations, limiting the scalability of kidney health screening in resource-constrained environments. This study presents embedded engineering framework for non-invasive, breath-based CKD staging framework supported by machine learning and implemented on a low-cost embedded platform. To account for physiological sex differences in baseline creatinine production, estimated glomerular filtration rate (eGFR) values and breath ammonia concentrations were derived from two independent clinical cohorts using sex-specific MDRD equations (incorporating the standard male formula and the $0.742$ female correction factor, respectively) and creatinine–BUN conversion models, with male- and female-parameterized algorithms developed in parallel. The resulting feature space was analyzed using four unsupervised clustering approaches to stratify subjects into five clinically meaningful kidney function stages. Stage-specific ammonia thresholds were implemented within an Arduino Nano-based prototype equipped with an MQ-137 gas sensor and OLED display, enabling real-time point-of-care classification. Dataset-level classification accuracy reached 82% for the male algorithm and 92% for the female algorithm. Hospital-based validation on 29 patients (22 male, 7 female) yielded a real-world testing accuracy of 90.5% ($20/22$) for male patients and 71.4% ($5/7$) for female patients, a discrepancy largely attributable to the small female sample size. Because the current evaluation lacks healthy control subjects and is constrained by sample size, these empirical results serve primarily to demonstrate hardware-software functional integration and real-world deployment feasibility rather than definitive clinical efficacy. Despite these preliminary, sample-limited clinical datasets, results suggest this approach holds promise as an accessible, non-invasive screening complement to conventional diagnostic pathways, particularly in low-resource healthcare settings. Full article

Background: Post-surgical breast cancer (BC) survivors frequently experience clusters of persistent post-treatment physical and psychological symptoms, which can negatively affect their quality of life (QoL). Mind–body interventions such as Qigong may offer potential benefit, but evidence in oncology remains limited. Methods: We conducted a single-arm pilot feasibility study at the AMICO clinic (IRCCS-CROB, Southern Italy) to evaluate the feasibility, acceptability, safety, and exploratory pre–post symptoms associated with an 8-week Qigong programme in post-surgical BC patients. Fourteen women (aged 42–73 years; stage I–III) who reported symptom burden and emotional sensitivity attended weekly one-hour classes and were encouraged to practise at home. Feasibility outcomes included adherence, completion of post-intervention assessment, adverse events, and participant acceptability. Symptom severity was assessed at baseline and post-intervention using a 0–5 study-specific symptom questionnaire score. For the secondary descriptive prevalence analysis, scores ≥ 3 were classified as indicating moderate-to-severe symptom burden. Results: All 14 participants completed the post-intervention assessment, overall intervention adherence was 92%, and no adverse events were reported. In the secondary descriptive analysis, the prevalence of moderate-to-severe pain and mood changes decreased from 64.3% to 35.7%, fatigue from 57.1% to 28.6%, anxiety from 85.7% to 42.9%, and sleep disturbances from 35.7% to 14.3%. Hot flushes decreased from 64.3% to 42.9%. Despite the heterogeneity of individual symptom trajectories, most participants reported meaningful improvements in overall well-being. Conclusions: The 8-week programme was feasible, well tolerated, and acceptable in this small real-world cohort. Symptom burden decreased during the intervention period; however, because of the uncontrolled study design and small sample size, these changes cannot be attributed specifically to Qigong. Larger controlled studies are required to estimate treatment effects and identify potential moderators of response. Full article

Background: Colonoscopy is the gold-standard screening modality for colorectal cancer (CRC) prevention, enabling detection and endoscopic resection of premalignant polyps and reducing CRC incidence and mortality by up to 77% and 53%, respectively. However, colonoscopy effectiveness is substantially dependent on endoscopist expertise, with significant inter-operator variability in adenoma detection rates (ADR) and, consequently, a risk of missed lesions, particularly diminutive and morphologically subtle adenomas. Recent advances in artificial intelligence (AI), specifically computer-aided detection (CADe) and computer-aided diagnosis (CADx) systems utilizing deep learning convolutional neural networks, have emerged as promising technologies to standardize lesion detection accuracy and reduce adenoma miss rates. Methods: A focused narrative literature review was conducted examining randomized controlled trials, meta-analyses, and implementation studies evaluating AI-assisted colonoscopy systems across diverse clinical populations and healthcare settings. Results: Evidence demonstrates that CADe systems consistently improve ADR, particularly for diminutive polyps and morphologically challenging lesions, though superiority over expert endoscopists remains inconsistent. CADx systems reliably meet ASGE-PIVI performance thresholds for diminutive polyp characterization, supporting implementation of resect-and-discard and diagnose-and-leave strategies. However, substantial heterogeneity exists regarding real-world effectiveness, cost-effectiveness, and optimal implementation frameworks across diverse settings. Conclusions: While AI-assisted colonoscopy demonstrates clinical promise in improving lesion detection and enabling optical diagnosis, realizing durable population-level benefit requires the establishment of standardized validation methodologies, large-scale pragmatic trials with patient-centered outcomes, robust regulatory frameworks, and equitable implementation strategies addressing health disparities globally. Full article

With the advent of the new definition, fatty liver disorders have been reframed into metabolic dysfunction-associated steatotic liver disease (MASLD), alcohol-related liver disease (ALD), and the mixed phenotype referred to as MetALD (MASLD and increased alcohol intake). This change reflects the real-world clinical practice, where metabolic dysfunction and alcohol frequently coexist and synergize to increase risks of steatohepatitis, fibrosis, and hepatocellular carcinoma (HCC). While conventional non-invasive tests (NITs) remain the backbone of risk stratification, lipidomics and metabolomics can capture biological information on disease mechanisms and may improve early detection and prognosis. Here, we summarize the current evidence on circulating and tissue lipidomic and metabolomic signatures across MASLD, ALD and MetALD, discuss how the new definitions affect clinical risk assessment, and highlight recent studies which partially distinguish molecular fingerprints for mixed etiology disease. Full article

Continual learning has made significant progress toward enabling adaptive machine learning under evolving environments, yet real-world deployment increasingly exposes systems to persistent harsh conditions, including distributional shifts, feature evolution, delayed or scarce supervision, imbalance, noise, and recurring or novel classes. While prior research has largely addressed these challenges in isolation, growing environmental complexity motivates a broader rethinking of continual adaptation as a self-regulating process rather than solely a parameter update problem. Building upon the emerging framework of Self-Adaptive Learning (SAL), this perspective explores how learning systems may progress beyond reactive adaptation toward autonomous recognition, policy selection, and context-sensitive regulation of learning behavior under persistent uncertainty. Rather than proposing a specific algorithmic solution, we position SAL as a conceptual systems framework for organizing future research on resilient, long-lived machine learning systems. We discuss key implications for deployment robustness, evaluation, safety, and adaptive governance, while outlining major open challenges in developing practical self-regulating learners. By strengthening SAL as a forward-looking framework, this work aims to advance the broader conversation on machine learning systems capable of sustained autonomy in dynamic real-world environments. Full article

LiDAR SLAM relies on reliable geometric constraints to estimate sensor motion and maintain consistent maps in complex three-dimensional environments. Planar features are commonly used for LiDAR registration, but repeatedly fitting local planes from neighboring points brings extra computation and may be sensitive to noisy observations. Voxel-plane maps address this issue by storing planar structures in voxel cells, yet most existing methods still construct planes and assign points to voxels according to the nominal point coordinates. When LiDAR measurement noise and pose prediction errors are ignored, plane parameters may be biased and points may be associated with unsuitable voxels. This paper presents UVP-LIO, an uncertainty-aware voxel-plane mapping method for LiDAR-inertial odometry. The measurement uncertainty of each LiDAR point and the uncertainty from state estimation are jointly propagated to the world frame to obtain a point-wise covariance. This covariance is then used in uncertainty-aware voxel association and covariance-weighted incremental plane updating. Plane thickness is further introduced to weight point-to-plane residuals during registration. Experiments in a LiDAR-only configuration on KITTI and in a LiDAR-inertial configuration on M3DGR show that UVP-LIO improves trajectory consistency and mapping robustness, especially in scenes with weak or ambiguous geometric constraints, while maintaining real-time performance. Full article

Background: Medication non-adherence is a major driver of poor therapeutic outcomes, and computer vision methods that observe facial movements offer a non-contact route to verifying oral medication intake. Objective: To map and synthesize the existing literature on computer vision techniques applicable to the monitoring of medication intake, focusing on face tracking methods, oral movement detection and deglutition recognition, and to assess their potential in supporting automatic medication adherence verification systems. Eligibility criteria: Peer-reviewed articles, conference papers, patents, theses and preprints published from 2016 onward, written in English or Portuguese, applying facial landmark tracking or face analysis to ingestion-related movements (mouth opening, hand-to-mouth motion, pill placement, mastication or deglutition); studies confined to object/pill detection without facial analysis, or to general food intake without transferability to medication, were excluded. Sources of evidence: A systematic screening of 362 initial records was conducted across six main electronic databases and repositories: Google Scholar, PubMed, ScienceDirect, arXiv, IEEE Xplore, and Espacenet. Charting methods: Data were charted with a standardized, pilot-tested extraction form capturing bibliographic attributes, dataset type, experimental environment, face tracking approach, tools/models, and target movements; extraction was performed by a single reviewer. Following the screening process, a final selection of 34 relevant studies was included for detailed analysis and mapping. Results: Among the 34 included studies, 14 employ facial landmarks, 11 utilize temporal deep learning models, 6 apply facial action models and 3 rely on hybrid multimodal approaches that combine video analysis, object detection and temporal modeling. Tasks such as detecting mouth opening or tracking pill-to-mouth movement show promising results, while accurately detecting deglutition remains a technical challenge due to high sensitivity and individual variability. Limitations: The majority of the literature relies on private or institutional datasets (31 studies) and operates in controlled laboratory environments (22 studies); only 2 studies evaluated their methods via independent external datasets, which limits the generalization of current solutions to real-world telemonitoring scenarios. Conclusions: The literature indicates the existence of solid technical foundations for developing automated medication intake verification systems. To advance the field toward practical deployment, future research must address the need for more diverse datasets, real-world validation and more robust, adaptable modeling frameworks. Full article