Search Results (8,671)

Background/Objectives: Cardiovascular diseases (CVDs) remain the leading cause of morbidity and mortality worldwide and increasingly are recognized as a continuum of interconnected conditions rather than isolated entities. Methods: A structured narrative literature search was performed in PubMed, Scopus, and Google Scholar for publications from 2015 to 2025 using combinations of different keywords: “cardiovascular disease spectrum”, “multi-omics”, “precision cardiology”, “machine learning”, and “artificial intelligence in cardiology”. Results: Evidence was synthesized across seven major clusters of cardiovascular conditions, and across these domains, common biological pathways were mapped onto heterogeneous clinical phenotypes, and we summarize how multi-omics integration, AI-enabled imaging and digital tools contribute to improved risk prediction and more informed clinical decision-making within this spectrum. Conclusions: Interpreting cardiovascular conditions as components of a shared disease spectrum clarifies cross-disease interactions and supports a shift from organ- and syndrome-based classifications toward mechanism- and data-driven precision cardiology. The convergence of multi-omics, and AI offers substantial opportunities for earlier detection, individualized prevention, and tailored therapy, but requires careful attention to data quality, equity, interpretability, and practical implementation in routine care. Full article

Natural language processing is the technology used to interact with computers using human languages. An overlapping technology is Information Retrieval (IR), in which a user searches for the demanded or required documents from among a number of documents that are already stored. The required document is retrieved according to the relevance of the query of the user, and the results are presented in descending order. Many of the languages have their own IR systems, whereas a dedicated IR system for Sindhi still needs attention. Various approaches to effective information retrieval have been proposed. As Sindhi is an old language with a rich history and literature, it needs IR. For the development of Sindhi IR, a document database is required so that the documents can be retrieved accordingly. Many Sindhi documents were identified and collected from various sources, such as books, journal, magazines, and newspapers. These documents were identified as having potential for use in indexing and other forms of processing. Probabilistic modeling and pattern discovery were used to find patterns and for effective retrieval and relevancy. The results for Sindhi Information Retrieval systems are promising and presented more than 90% relevancy. The time elapsed was recorded as ranging from 0.2 to 4.8 s for a single word and 4.6 s with a Sindhi sentence, with the same starting time of 0.2 s. The IR system for Sindhi can be fine-tuned and utilized for other languages with the same characteristics, which adopt Arabic script. Full article

The computational tractability of Integrated Information Theory (IIT) is fundamentally constrained by the exponential cost of identifying the Minimum Information Partition (MIP), which is required to quantify integrated information (Φ). Existing approaches become impractical beyond ~15–20 variables, limiting IIT analyses on realistic neural and complex systems. We introduce GeoMIP, a geometric–topological framework that recasts the MIP search as a graph-based optimization problem on the n-dimensional hypercube graph: discrete system states are modeled as graph vertices, and Hamming distance adjacency defines edges and shortest-path structures. Building on a tensor-decomposed representation of the transition probabilities, GeoMIP constructs a transition-cost (ground cost) structure by dynamic programming over graph neighborhoods and BFS-like exploration by Hamming levels, exploiting hypercube symmetries to reduce redundant evaluations. We validate GeoMIP against PyPhi, ensuring reliability of MIP identification and Φ computation. Across multiple implementations, GeoMIP achieves 165–326× speedups over PyPhi while maintaining 98–100% agreement in partition identification. Heuristic extensions further enable analyses up to ~25 variables, substantially expanding the practical IIT regime. Overall, by leveraging the hypercube’s explicit graph structure (vertices, edges, shortest paths, and automorphisms), GeoMIP turns an intractable combinatorial search into a scalable graph-based procedure for IIT partitioning. Full article

The Rate of Penetration (ROP), as a core indicator for evaluating drilling efficiency, holds significant importance for optimizing drilling parameter configurations, enhancing drilling efficiency, and reducing operational costs. To address the limitations of existing ROP prediction models—such as difficulties in modeling, solution complexity, and inefficient utilization of field big data—this paper proposes a Bayesian-Optimized LSTM-based ROP prediction model with fused feature selection (BO-LSTM-FS). The model innovatively introduces a sequential-cross-validation fused feature selection framework, which organically integrates Pearson correlation analysis, variance filtering, and mutual information, and incorporates a forward search strategy for final validation. Building on this, the Bayesian optimization algorithm is employed for systematic global optimization of the key hyperparameters of the LSTM neural network. Experimental results demonstrate that the BO-LSTM-FS model achieves significant performance improvements compared to traditional Backpropagation (BP) neural networks, standard LSTM neural networks, and CNN-LSTM models: Mean Absolute Error (MAE) is reduced by 48.0%, 29.3%, and 23.5%, respectively; Root Mean Square Error (RMSE) by 45.5%, 38.5%, and 32.2%, respectively; Mean Absolute Percentage Error (MAPE) by 47.8%, 29.4%, and 22.6%, respectively; and the Coefficient of Determination (R²) is increased by 8.6%, 4.4%, and 3.0%, respectively. The model exhibits high prediction accuracy, fast convergence speed, and strong generalization capability, providing a scientific reference for improving the Rate of Penetration in practical drilling operations. Full article

Background/Objectives: Within Paediatric Palliative Care (PPC), motherhood in the context of severe cognitive impairment is shaped by unique emotional, relational, and identity-related challenges. Traditional understandings of maternal identity are strained when verbal communication and typical developmental milestones are absent. Although caregiving in PPC has been widely studied, the subjective and symbolic dimensions of motherhood in this setting have received far less attention. This study sought to explore how mothers construct, interpret, and make sense of their maternal identity while caring for a child with severe cognitive impairment in a PPC context, and to underscore the clinical relevance of these identity-related processes. Methods: A qualitative study was conducted involving nine mothers of children receiving paediatric palliative care services at a regional centre in Italy. Participants engaged in three online focus groups, totalling 270 min. Reflexive thematic analysis was employed to interpret the transcribed data, using ATLAS.ti software, version 25.0.1 ATLAS.ti Scientific Software Development GmbH, Berlin, Germany, for support. Member reflections were incorporated to validate the findings. Results: Three interconnected themes emerged from the reflexive thematic analysis. First, mothers described the development of a fusion-like, enmeshed mother–child relationship, characterised by embodied attunement, specialised interpretive expertise, and lifelong care dependency. Second, mothers detailed the construction of their maternal role, shaped by emotional labour, identity negotiation, sacrifice, loneliness, and peer support, alongside the construction of the child’s role, in which children were perceived as unique, symbolically meaningful beings whose social presence and limited reciprocity shaped maternal identity. Third, mothers articulated a search for meaning that sustained them throughout the caregiving journey, reframing their experience within a broader existential and relational perspective. Conclusions: Maternal caregiving in PPC encompasses distinct emotional, relational, and symbolic dimensions that extend beyond conventional understandings of motherhood. Grasping these identity-related dynamics has direct clinical relevance: it enables more attuned communication, strengthens the therapeutic alliance, and supports personalised, meaning-oriented care. These insights highlight the need for tailored interventions and further qualitative research to inform health care professionals and interdisciplinary practice. Full article

Background: Plantar fasciitis is a prevalent musculoskeletal disease characterized by heel pain and functional impairment. Both high-intensity laser therapy (HILT) and extracorporeal shockwave therapy (ESWT) have demonstrated efficacy in managing plantar fasciitis; however, their relative effectiveness remains unclear. Purpose: This systematic review and meta-analysis aimed to compare the effects of HILT and ESWT for treating plantar fasciitis. Methods: A comprehensive literature search of PubMed, the Cochrane Library, EMBASE, and Scopus was conducted from inception to 13 July 2025 to identify randomized controlled trials (RCTs) investigating both interventions. Two reviewers independently extracted data and assessed the methodological quality of the trials using the Physiotherapy Evidence Database (PEDro) scale. The certainty of evidence was evaluated using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. The primary outcomes of this study were pain intensity and foot function. The visual analog scale (VAS) was used for pain assessment. Foot function was evaluated by the total scores of the Foot Function Index (FFI) and American Orthopedic Foot & Ankle Society Scale (AOFAS) and the activities of daily living (ADL) subscale scores of the Foot and Ankle Ability Measure (FAAM). Outcomes were assessed at the end of treatment and during short-, medium-, and long-term follow-ups. The meta-analysis utilized standardized mean differences (SMDs), assessed heterogeneity using the I² test, applied the inverse variance method for pooling continuous variables, and employed a random-effects model because of the variable study methods used across the included articles. Results with p < 0.05 were considered statistically significant. The I² test was used to objectively measure statistical heterogeneity, with I² ≥ 50% indicating significant heterogeneity. Results: Five RCTs met the inclusion criteria, with methodological quality scores ranging from 6 to 7 on the 10-point PEDro scale. In total, 120 participants received HILT and 116 received ESWT. Regarding pain intensity (VAS), no statistically significant differences were detected between HILT and ESWT at any time point, including short-term morning pain (SMD = −0.11, 95% CI −0.42 to 0.19, p = 0.40), resting pain (SMD = 0.01, 95% CI −0.48 to 0.49, p = 0.05), and activity pain (SMD = −0.08, 95% CI −0.41 to 0.26, p = 0.89), as well as medium-term morning, resting, and activity pain (all p > 0.05). For foot function (FFI), the pooled analysis of all studies showed no significant short-term difference (SMD = 0.37, 95% CI −0.22 to 0.95, p = 0.01; I² = 73%); however, a subsequent sensitivity analysis, which excluded one studyreduced heterogeneity to 0% and revealed a significant short-term advantage of ESWT (SMD = 0.64, 95% CI 0.32 to 0.95, p < 0.01). Medium-term FFI also favored ESWT (SMD = 0.53, 95% CI 0.14 to 0.92, p < 0.01). Overall, the certainty of evidence ranged from moderate to low, mainly due to risk of bias and heterogeneity, as assessed by the GRADE approach. Conclusions: While the pooled results suggested a trend toward greater functional improvement with ESWT than with HILT in the short- and medium-term, the effect sizes were small. No significant between-group differences were observed in pain-related outcomes. Given the limited number of available trials and variability in treatment protocols, current evidence remains insufficient to draw definitive conclusions about the comparative efficacy of ESWT and HILT. Further high-quality, large-scale randomized controlled trials with standardized methodologies are needed to better inform clinical decision-making. Full article

Informal learning in institutional settings plays a vital role in lifelong education by fostering self-directed knowledge acquisition. With the increasing integration of digital media into these environments, augmented reality (AR) has emerged as a particularly promising technology due to its ability to overlay virtual content in real-time and across multiple sensory modalities. This systematic literature review investigates the use of AR in institutional informal learning places (IILPs) from 2018 to 2025, aiming to synthesize findings across the following overall research questions: (1) In which IILP contexts has AR been implemented, and what are the characteristics of the technology? (2) What learning-relevant functions and (3) outcomes are associated with AR in these settings? (4) Which learning theories underpin the design of AR interventions? Following the PRISMA guidelines, empirical studies were identified through comprehensive database searches (Scopus, Web of Science, IEEE Xplore, FIS Bildung) and cross-referencing. Forty-four studies were analyzed via qualitative content analysis. The goal is to provide a descriptive overview of findings, patterns, and relationships. Findings indicate that AR is widely adopted across diverse domains and institutional contexts, primarily through mobile-based AR applications for K–12 learning. Native app development signals growing technological maturity. AR enhances both cognitive and emotional-motivational outcomes, though its potential to support social interaction remains insufficiently investigated. The predominant function of AR is the provision of information. Most of the examined studies are grounded in constructivist or cognitivist learning theories, particularly the Cognitive Theory of Multimedia Learning. Only limited references to emotional-motivational frameworks and minimal references to behaviorist frameworks were found. Full article

Women of reproductive age, especially those with polycystic ovarian syndrome (PCOS), often use glucagon-like peptide-1 receptor agonists (GLP-1RAs) to improve their metabolic functions. A growing body of evidence suggests that GLP-1R signaling may directly affect ovarian physiology, influencing granulosa cell proliferation, survival pathways, and steroidogenic production, in addition to its systemic metabolic effects. Nonetheless, there is a limited comprehension of the molecular mechanisms that regulate these activities and their correlation with menstrual function, reproductive potential, and assisted reproduction. This comprehensive review focuses on ovarian biology, granulosa cell signaling networks, steroidogenesis, and translational fertility outcomes, integrating clinical, in vivo, and in vitro information to elucidate the effects of GLP-1 receptor agonists on reproductive health. We conducted a thorough search of PubMed, Scopus, and Web of Science for randomized trials, prospective studies, animal models, and cellular experiments evaluating the effects of GLP-1RA on reproductive or ovarian outcomes, in accordance with PRISMA criteria. The retrieved data included metabolic changes, androgen levels, monthly regularity, ovarian structure, granulosa cell growth and death, FOXO1 signaling, FSH-cAMP-BMP pathway activity, and fertility or IVF results. Clinical trials shown that GLP-1 receptor agonists improve menstrual regularity, decrease body weight and central adiposity, increase sex hormone-binding globulin levels, and lower free testosterone in overweight and obese women with PCOS. Liraglutide, when combined with metformin, significantly improved IVF pregnancy rates, whereas exenatide increased natural conception rates. Mechanistic studies demonstrate that GLP-1R activation affects FOXO1 phosphorylation, hence promoting granulosa cell proliferation and anti-apoptotic processes. Incretin signaling altered steroidogenesis by reducing the levels of StAR, P450scc, and 3β-HSD, so inhibiting FSH-induced progesterone synthesis, while simultaneously enhancing BMP-Smad signaling. Animal studies demonstrated both beneficial (enhanced follicular growth, anti-apoptotic effects) and detrimental results (oxidative stress, granulosa cell death, uterine inflammation), indicating a context- and dose-dependent response. GLP-1 receptor agonists influence female reproductive biology by altering overall physiological processes and specifically impacting the ovaries via FOXO1 regulation, steroidogenic enzyme expression, and BMP-mediated FSH signaling. Preliminary clinical data indicate improved reproductive function in PCOS, as seen by increased pregnancy rates in both natural and IVF cycles; nevertheless, animal studies reveal a potential risk of ovarian and endometrial damage. These results highlight the need for controlled human research to clarify reproductive safety, molecular pathways, and optimum therapy timing, particularly in non-PCOS patients and IVF settings. Full article

To address the current reliance on manual labor in traditional logistics sorting operations, which leads to low sorting efficiency and high operational costs, this study presents the design of an unmanned logistics vehicle based on the Robot Operating System (ROS). To overcome bounding-box loss issues commonly encountered by mainstream video-stream image segmentation algorithms under complex conditions, the novel SMITE video image segmentation algorithm is employed to accurately extract key regions of mail items while eliminating interference. Extracted logistics information is mapped to corresponding grid points within a map constructed using Simultaneous Localization and Mapping (SLAM). The system performs global path planning with the A* heuristic graph search algorithm to determine the optimal route, autonomously navigates to the target location, and completes the sorting task via a robotic arm, while local path planning is managed using the Dijkstra algorithm. Experimental results demonstrate that the SMITE video image segmentation algorithm maintains stable and accurate segmentation under complex conditions, including object appearance variations, illumination changes, and viewpoint shifts. The PaddleOCR text recognition algorithm achieves an average recognition accuracy exceeding 98.5%, significantly outperforming traditional methods. Through the analysis of existing technologies and the design of a novel parcel-grasping control system, the feasibility of the proposed system is validated in real-world environments. Full article

Background: Dysglycemia remains a persistent challenge in hospital care. Despite advances in outpatient diabetes technology, inpatient insulin management largely depends on intermittent point-of-care glucose testing, static insulin dosing protocols and rule-based decision support systems. Artificial intelligence (AI) offers potential to transform this care through predictive modeling and adaptive insulin control. Methods: Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines, a scoping review was conducted to characterize AI algorithms for insulin dosing and glycemic management in hospitalized patients. An interdisciplinary team of clinicians and engineers reached consensus on AI definitions to ensure inclusion of machine learning, deep learning, and reinforcement learning approaches. A librarian-assisted search of five databases identified 13,768 citations. After screening and consensus review, 26 studies (2006–2025) met the inclusion criteria. Data were extracted on study design, population, AI methods, data inputs, outcomes, and implementation findings. Results: Studies included ICU (N = 13) and general ward (N = 9) patients, including patients with diabetes and stress hyperglycemia. Early randomized trials of model predictive control demonstrated improved mean glucose (5.7–6.2 mmol/L) and time in target range compared with standard care. Later machine learning models achieved strong predictive accuracy (AUROC 0.80–0.96) for glucose forecasting or hypoglycemia risk. Most algorithms used data from Medical Information Mart for Intensive Care (MIMIC) databases; few incorporated continuous glucose monitoring (CGM). Implementation and usability outcomes were seldom reported. Conclusions: Hospital AI-driven models showed strong algorithmic performance but limited clinical validation. Future co-designed, interpretable systems integrating CGM and real-time workflow testing are essential to advance safe, adaptive insulin management in hospital settings. Full article

The economic dispatch (ED) problem is a major challenge in power system optimization. In this article, an Enhanced Social Group Optimization (ESGO) algorithm is presented for solving the economic dispatch problem with or without wind units, considering various characteristics related to valve-point effects, ramp-rate constraints, prohibited operating zones, and transmission power losses. The Social Group Optimization (SGO) algorithm models the social dynamics of individuals within a group—through mechanisms of collective learning, behavioral adaptation, and information exchange—and leverages these interactions to guide the population efficiently towards optimal solutions. ESGO extends SGO along three complementary directions: redefining the update relations of the original SGO, introducing stochastic operators into the heuristic mechanisms, and dynamically updating the generated solutions. These modifications aim to achieve a more robust balance between exploration and exploitation, enable flexible adaptation of search steps, and rapidly integrate improved-fitness solutions into the evolutionary process. ESGO is evaluated in six distinct cases, covering systems with 6, 40, 110, and 220 units, to demonstrate its ability to produce competitive solutions as well as its performance in terms of stability, convergence, and computational efficiency. The numerical results show that, in the vast majority of the analyzed cases, ESGO outperforms SGO and other known or improved metaheuristic algorithms in terms of cost and stability. It incorporates wind generation results at an operating cost reduction of approximately 10% compared to the thermal-only system, under the adopted linear wind power model. Moreover, relative to the size of the analyzed systems, ESGO exhibits a reduced average execution time and requires a small number of function evaluations to obtain competitive solutions. Full article

Vasculitides are a heterogeneous group of disorders characterized by inflammation of blood vessel walls, leading to tissue ischemia and organ injury. Traditional inflammatory markers such as the erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP) are widely used but lack diagnostic specificity. This has driven the search for more informative biomarkers across vasculitis subtypes. This review summarizes current evidence for validated and emerging biomarkers in large-, medium-, small-, and variable-vessel vasculitis, as well as single-organ vasculitis. Key analytes reflect systemic inflammation, such as serum amyloid A (SAA) and interleukin-6 (IL-6), as well as endothelial activation, complement pathways, neutrophil and macrophage activation, and organ-specific damage. Promising candidates include pentraxin-3 (PTX3) and matrix metalloproteinase-9 (MMP-9) in large-vessel vasculitis; N-terminal pro-B-type natriuretic peptide (NT-proBNP) and S100 proteins in Kawasaki disease; galactose-deficient immunoglobulin A1 (Gd-IgA1) and urinary angiotensinogen (AGT) in IgA vasculitis; and tissue inhibitor of metalloproteinases-1 (TIMP-1), S100 proteins, complement C3, and PTX3 in antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis. Although these biomarkers provide mechanistic insight, most lack disease-specificity, external validation, or standardized assays. Future progress will require multicenter studies, harmonized testing, and integrated biomarker panels combined with imaging modalities to improve diagnosis, activity assessment, and monitoring. Full article

Type 2 diabetes is increasingly prevalent among children and adolescents with overweight or obesity, and although lifestyle interventions remain first-line preventive strategies, long-term adherence and effectiveness are often limited. Metformin has demonstrated efficacy in delaying type 2 diabetes onset in adults at high risk, but its preventive role in pediatric populations remains unclear. This systematic review and meta-analysis aims to evaluate the effectiveness of metformin, alone or in combination with lifestyle interventions, in preventing or delaying type 2 diabetes among children and adolescents with overweight or obesity. The protocol is registered in PROSPERO (CRD42024615622), MEDLINE (PubMed), Embase, Cochrane Library, Scopus, and Web of Science and will be searched from inception to June 2025. Eligible studies include randomized controlled trials, quasi-experimental studies, and prospective cohort studies involving individuals under 18 years of age. The primary outcome is incidence of type 2 diabetes, with secondary outcomes including fasting plasma glucose, HbA1c, insulin resistance, BMI z-score, adherence, and adverse events. Where appropriate, random-effects meta-analyses will be conducted. This review will synthesize current evidence on metformin for pediatric type 2 diabetes prevention and inform future preventive strategies and clinical decision-making. Full article

The One Health framework highlights the interconnectedness of human, animal, and environmental health, requiring interdisciplinary and multisectoral collaboration to address complex global health challenges. This scoping review protocol aims to guide the systematic mapping on how studies and policy initiatives have incorporated socioecological interconnections within the One Health paradigm, following the Joanna Briggs Institute guidance and the PRISMA Scr checklist. The experimental design includes searches in PubMed, Scopus, Web of Science, LILACS, Health Systems Evidence, Social Systems Evidence, and Google Scholar for the period from 2004 to 2025. The strategy, developed with librarian support and peer reviewed, includes terms in English, Portuguese, and Spanish. Pilot searches retrieved 5333 PubMed and 470 LILACS records. Eligible documents must explicitly present two or more of the six One Health dimensions: policies to strengthen health systems; antimicrobial resistance; food safety; environmental health; emerging and re-emerging zoonotic epidemics and pandemics; endemic zoonotic, neglected tropical and vector-borne diseases. A standardized tool was developed for data extraction, synthesizing in narrative, tabular, and graphical formats. The protocol’s utilization will provide comprehensive mapping of practices and policies, identifying achievements, barriers, and knowledge gaps to inform future strategies and strengthen global health governance. Full article

Background/Objectives: Sexual dysfunction (SD) and broader sexual health problems are common after neurological disorders, yet interventional evidence is fragmented across conditions and outcomes. This umbrella review mapped and appraised systematic review-level evidence on interventions targeting SD and sexual health in neurological populations and qualified conclusions using certainty of evidence. Methods: PubMed, Web of Science, Cochrane Library, Embase, PsycINFO, EBSCOhost, and Scopus were searched from inception to 27 November 2025. Two reviewers screened records, extracted data, assessed review quality with AMSTAR 2, and rated certainty across intervention–outcome pairings using a GRADE-informed approach that integrated review confidence and primary-study risk-of-bias as reported by the source reviews. Results: Twenty-six systematic reviews were included. Overall confidence was frequently limited (17/26 critically low and 6/26 low), with only a small subset rated moderate or higher. Evidence was most coherent for phosphodiesterase type 5 (PDE5) inhibitors improving erectile function in men with spinal cord injury, whereas most other interventions and outcomes were supported by low or very low certainty. Women were represented in 16/26 reviews, yet validated female sexual function outcomes were synthesized in 6/26 reviews and relationship/couple outcomes in 3/26; furthermore, 10/26 reviews restricted inclusion to men, and no review synthesized pediatric intervention trials. Conclusions: Evidence supports PDE5 inhibitors for improving erectile function in men with spinal cord injury, while evidence for other interventions and sexual health domains remains limited. Methodological limitations highlight the need for more inclusive trials, broader standardized outcomes, and longer follow-up within neurorehabilitation pathways. Full article