Search Results (4,695)

Background: Stroke is a leading cause of seizures and epilepsy, both of which are linked to increased mortality, disability, and hospital readmissions. Early recognition and management of seizures in acute stroke are crucial for improving outcomes. Electroencephalogram (EEG) is not routinely used for post-stroke seizure monitoring and is typically initiated only after clinical suspicion arises, making bedside recognition essential. This scoping review aimed to map the existing literature on clinical methods used for identifying and observing early post-stroke seizures (EPSSs) at the bedside. Methods: We included literature involving adults with acute ischaemic stroke or primary intracerebral haemorrhage who were diagnosed or suspected of having inpatient EPSS. Searches were conducted in Medline, CINAHL, Embase, and the Cochrane Library for English-language publications up to April 2023. Eligible sources included primary research, case reports, systematic reviews, clinical guidelines, consensus statements, and expert opinion. Reference lists of included articles were also reviewed. Data were charted and synthesised to assess the scope, type, and gaps in the evidence. Results: Thirty papers met inclusion criteria: 17 research studies, six expert opinions, four case reports, and three clinical guidelines. Empirical evidence on clinical methods for seizure recognition and monitoring in acute stroke was limited. No studies evaluated the effectiveness of different approaches, and existing recommendations lacked detail and consensus. Conclusions: Accurate EPSS diagnosis is vital due to its impact on outcomes. This review highlights inconsistency in monitoring methods and a clear need for targeted research into effective clinical identification strategies in acute stroke care. Full article

Background: Bioelectrical impedance analysis (BIA) is a widely used field technique for assessing body composition in football. However, its reliance on population-specific regression equations limits its accuracy. Objective: This scoping review aimed to map the scientific literature on BIA applications in professional and semi-professional football, highlighting uses, limitations, and research opportunities. Methods: A comprehensive search was conducted in the scientific databases PubMed, EMBASE, Web of Science, and SPORTDiscus. Identified studies involved the use of BIA in professional and semi-professional football players (≥16 years) in the context of routine training and competition. Results: From 14,624 records, 39 studies met the inclusion criteria and were included. Three main applications were identified: (1) quantitative body composition assessment, (2) qualitative/semi-quantitative analysis (e.g., bioelectrical impedance vector analysis (BIVA)), and (3) muscle health and injury monitoring. Seven specific research areas emerged, including hydration monitoring, cross-method validation of body composition analyses, development of predictive models, sport phenotype identification, tracking training adaptations, performance/load assessment via phase angle, and localized BIA for injury diagnosis and recovery. Conclusions: While quantitative BIA estimates may lack individual-level precision, raw parameter analyses may offer valuable insights into hydration, cellular integrity, and muscle injury status, yet further research is needed to fully realize these applications. Full article

Background/Objectives: Migrant youth often experience multiple, intersecting systems of oppression (e.g., racism, poverty, and discrimination) that may contribute to disparities in pediatric pain prevalence, severity, and management. However, pain in migrant youth remains poorly understood. This rapid scoping review will examine the nature and extent of the existing literature on pain among migrant youth. Methods: This protocol has been preregistered on the Open Science Framework. The review will follow guidelines for conducting and reporting rapid and scoping reviews, and will be guided by PCC (population, concept, context) and PROGRESS-Plus methodological frameworks. Electronic searches will be conducted in MEDLINE, CINAHL, and Scopus for primary research studies published since 2015 that describe and examine pain among migrant youth (age < 18 years). Two reviewers will independently screen titles, abstracts, and full texts, with disagreements resolved by consensus or a third reviewer. Data charting will be piloted on 5–10 studies, then independently conducted by two reviewers. Extracted data will include study characteristics (authors, year, purpose, methodology); participant sociodemographic information (e.g., racial and/or ethnic identity, age, sex, gender identity, sexual orientation, socioeconomic position); migration status; countries of origin and destination; definition and characteristics of pain; and measures of systemic factors (e.g., racism). Results: Findings will be synthesized descriptively and interpreted within sociocultural and geopolitical contexts to better understand pain among migrant youth. Conclusions: This review will aim to provide critical insights into the intersections between migration and pediatric pain, offering guidance for future research, clinical practice, and policy to improve pain management and outcomes for migrant youth. Full article

Aim: This review aims to validate the Experience-Based Onboarding Model (EBOM) and refine it into an improved adaptive onboarding model, OnMod. Methods: In this review, autoethnography is combined with a Multivocal Literature Review (MLR) that combines white and gray literature sources. Evidence is mapped to entities and semantic relations and assessed using predefined decision rules. Main findings: The validation of the model confirms the core EBOM entities and semantic relations. It also introduces several new or renamed entities or semantic relations that close the feedback loop and yield the refined OnMod model. Implications: The theoretical contribution is reflected in the application of autoethnography in combination with the MLR, where it represents a good basis for the development of an onboarding model. In industrial practice, the presented OnMod model can be used by mentors and managers as a guide for improving operational and daily activities, as well as for the development of onboarding strategies in IT and software companies. Full article

The lung is increasingly recognized as an organ with dual endocrine and respiratory roles, participating in a complex bidirectional crosstalk with systemic hormones and local/paracrine activity. Endocrine and paracrine pathways regulate lung development, ventilation, immunity, and repair, while pulmonary cells express hormone receptors and secrete mediators with both local and systemic effects, defining the concept of the “endocrine lung”. This narrative review summarizes current evidence on the endocrine–pulmonary axis. Thyroid hormones, glucocorticoids, sex steroids, and metabolic hormones (e.g., insulin, leptin, adiponectin) critically influence alveologenesis, surfactant production, ventilatory drive, airway mechanics, and immune responses. Conversely, the lung produces mediators such as serotonin, calcitonin gene-related peptide, endothelin-1, leptin, and keratinocyte growth factor, which regulate vascular tone, alveolar homeostasis, and immune modulation. We also describe the respiratory manifestations of major endocrine diseases, including obstructive sleep apnea and lung volume alterations in acromegaly, immunosuppression and myopathy in Cushing’s syndrome, hypoventilation in hypothyroidism, restrictive “diabetic lung”, and obesity-related phenotypes. In parallel, chronic pulmonary diseases such as chronic obstructive pulmonary disease, interstitial lung disease, and sleep apnea profoundly affect endocrine axes, promoting insulin resistance, hypogonadism, GH/IGF-1 suppression, and bone metabolism alterations. Pulmonary neuroendocrine tumors further highlight the interface, frequently presenting with paraneoplastic endocrine syndromes. Finally, therapeutic interactions are discussed, including the risks of hypothalamic–pituitary–adrenal axis suppression with inhaled corticosteroids, immunotherapy-induced endocrinopathies, and inhaled insulin. Future perspectives emphasize mapping pulmonary hormone networks, endocrine phenotyping of chronic respiratory diseases, and developing hormone-based interventions. Full article

Lung-protective ventilation and other experimental conditions raise arterial carbon dioxide tension (PaCO₂) and alter pH. Short-term benefits are reported in non-infectious settings, whereas infection and/or prolonged exposure are typically harmful. This scoping review systematically maps immune-mediated effects of hypercapnia on innate immunity and alveolar epithelial repair. Scoping review per Levac et al. and PRISMA Extension for Scoping Reviews (Open Science Framework protocol: 10.17605/OSF.IO/WV85T; post hoc). We searched original preclinical studies (in vivo/in vitro) in PubMed, Web of Science, ScienceDirect, Cochrane Reviews, and SciELO (2008–2023). PaCO₂ (mmHg) was prioritized; %Fraction of inspired Carbon Dioxide (%FiCO₂) was recorded when PaCO₂ was unavailable; pH was classified as buffered/unbuffered. Data were organized by context, PaCO₂, and exposure duration; synthesis used heat maps (0–120 h) and a narrative description for >120 h. Mechanistic axes extracted the following: NF-κB (canonical/non-canonical), Bcl-2/Bcl-xL–Beclin-1/autophagy, AMPK/PKA/CaMKKβ/ERK1/2 and ENaC/Na,K-ATPase trafficking, Wnt/β-catenin in AT2 cells, and miR-183/IDH2/ATP. Thirty-five studies met the inclusion criteria. In non-infectious models, a “protective window” emerged, with moderate PaCO₂ and brief exposure (65–95 mmHg; ≤4–6 h), featuring NF-κB attenuation and preserved epithelial ion transport. In infectious models and/or with prolonged exposure or higher PaCO₂, harmful signals predominated: reduced phagocytosis/autophagy (Bcl-2/Bcl-xL–Beclin-1 axis), AMPK/PKA/ERK1/2-mediated internalization of ENaC/Na,K-ATPase, depressed β-catenin signaling in AT2 cells, impaired alveolar fluid clearance, and increased bacterial burden. Chronic exposures (>120 h) reinforced injury. Hypercapnia is a context-, dose-, time-, and pH-dependent double-edged modulator. The safe window is narrow; standardized, parallel reporting of PaCO₂ and pH—with explicit comparisons of buffered vs. unbuffered hypercapnia—is essential to guide clinical translation. Full article

Animal welfare (AW) is increasingly being discussed and mandated in chicken production, both by current Brazilian legislation and by importing markets. Industries continually seek greater financial returns, and within this context, it has been observed that the proper implementation of animal welfare principles effectively reduces losses by minimizing carcass condemnations due to injuries, thereby significantly contributing to in-creased profitability. The economic impact of non-compliance with these welfare standards in broiler production is well documented in the scientific literature. However, the same level of concern is not observed regarding the financial impact on integrated producers, who supply the raw materials. The present study aims to systematically map, contextualize, quantify, and qualitatively analyze articles evaluating the implementation of animal welfare in industrial broiler production and its impact on the financial returns of producers and integrated companies. The primary descriptor used was “animal welfare.” To quantify the relevant articles, the Proknow-C method was applied, followed by a similarity analysis using VoSViewer® software version 1.6.19 for systematic content evaluation. Descriptor combinations were led by animal welfare, followed by broiler pro-duction, poultry production, slaughter, economy, and rural producers. Although a significant number of articles address AW, those focused exclusively on chicken production are far fewer, declining even more when carcass condemnations are considered. Only six studies included the descriptor “economic,” and just three included “producer.” The con-tent of these nine studies was systematically reviewed, with two excluded and seven selected for discussion. Among the seven analyzed studies, none specifically examined the economic impact of AW implementation for the producer, clearly highlighting a significant research gap. Full article

Background/Objectives: The integration of artificial intelligence (AI) into obstetric care poses significant potential to enhance clinical decision-making and optimize maternal and neonatal outcomes. Traditional prediction methods in maternal-foetal medicine often rely on subjective clinical judgment and limited statistical models, which may not fully capture complex patient data. By integrating computational innovation with mechanistic biology and rigorous clinical validation, AI can finally fulfil the promise of precision obstetrics by transforming pregnancy complications into a preventable, personalised continuum of care. This study aims to map the current landscape of AI applications across the continuous spectrum of maternal–foetal health, identify the types of models used, and compare clinical targets and performance, potential pitfalls, and strategies to translate innovation into clinical impact. Methods: A literature search of peer-reviewed studies that employ AI for prediction, diagnosis, or decision support in Obstetrics was conducted. AI algorithms were categorised by application area: foetal monitoring, prediction of preterm birth, prediction of pregnancy complications, and/or labour and delivery. Results: AI-driven models consistently demonstrate superior performance to traditional approaches. Nevertheless, their widespread clinical adoption is hindered by limited dataset diversity, “black-box” algorithms, and inconsistent reporting standards. Conclusions: AI holds transformative potential to improve maternal and neonatal outcomes through earlier diagnosis, personalised risk assessment, and automated monitoring. To fulfil this promise, the field must prioritize the creation of large, diverse, open-access datasets, mandate transparent, explainable model architectures, and establish robust ethical and regulatory frameworks. By addressing these challenges, AI can become an integral, equitable, and trustworthy component of Obstetric care worldwide. Full article

Artificial intelligence (AI) offers several opportunities in wildfire management, particularly for improving short- and long-term fire occurrence forecasting, spread modeling, and decision-making. When properly adapted beyond research into real-world settings, AI can significantly reduce risks to human life, as well as ecological and economic damages. However, despite increasingly sophisticated research, the operational use of AI in wildfire contexts remains limited. In this article, we review the main domains of wildfire management where AI has been applied—susceptibility mapping, prediction, detection, simulation, and impact assessment—and highlight critical limitations that hinder practical adoption. These include challenges with dataset imbalance and accessibility, the inadequacy of commonly used metrics, the choice of prediction formats, and the computational costs of large-scale models, all of which reduce model trustworthiness and applicability. Beyond synthesizing existing work, our survey makes four explicit contributions: (1) we provide a reproducible taxonomy supported by detailed dataset tables, emphasizing both the reliability and shortcomings of frequently used data sources; (2) we propose evaluation guidance tailored to imbalanced and spatial tasks, stressing the importance of using accurate metrics and format; (3) we provide a complete state of the art, highlighting important issues and recommendations to enhance models’ performances and reliability from susceptibility to damage analysis; (4) we introduce a deployment checklist that considers cost, latency, required expertise, and integration with decision-support and optimization systems. By bridging the gap between laboratory-oriented models and real-world validation, our work advances prior reviews and aims to strengthen confidence in AI-driven wildfire management while guiding future research toward operational applicability. Full article

Arrhythmias significantly contribute to morbidity and mortality in patients with congenital heart disease (CHD). While postoperative factors predisposing to arrhythmias are well-established, early electrophysiological alterations in pediatric CHD remain poorly understood. This review summarizes current knowledge on postnatal cardiac maturation, conduction-system development, and electrophysiological abnormalities in pediatric patients with and without CHD. Importantly, arrhythmia prevalence, mechanisms, and clinical relevance are systematically discussed across three pediatric groups, including healthy children and patients with unrepaired and repaired CHD. Understanding developmental arrhythmogenic mechanisms may facilitate early risk stratification, guide clinical management decisions, and improve long-term outcomes for pediatric patients with CHD. This review discusses the complex interplay between cardiac maturation, congenital defects, and arrhythmogenesis. It also outlines future directions that include noninvasive monitoring, selective intraoperative mapping, animal model studies, and standardized data collection to improve early risk stratification and long-term outcomes in children with CHD. Full article

Demand response (DR) is increasingly recognized as a critical flexibility resource for modernizing power systems, enabling the large-scale integration of renewable energy and enhancing grid stability. While the field of general electricity load forecasting is supported by numerous systematic reviews, the specific subfield of DR potential forecasting has received comparatively less synthesized attention. This gap leaves a fragmented understanding of modeling techniques, practical implementation challenges, and future research problems for a function that is essential for market participation. To address this, this paper presents a PRISMA-2020-compliant systematic review of 172 studies to comprehensively analyze the state-of-the-art in DR potential estimation. We categorize and evaluate the evolution of forecasting methodologies, from foundational statistical models to advanced AI architectures. Furthermore, the study identifies key technological enablers and systematically maps the persistent technical, regulatory, and behavioral barriers that impede widespread DR deployment. Our analysis demonstrates a clear trend towards hybrid and ensemble models, which outperform standalone approaches by integrating the strengths of diverse techniques to capture complex, nonlinear consumer dynamics. The findings underscore that while technologies like Advanced Metering Infrastructure (AMI) and the Internet of Things (IoT) are critical enablers, the gap between theoretical potential and realized flexibility is primarily dictated by non-technical factors, including inaccurate baseline methodologies, restrictive market designs, and low consumer engagement. This synthesis brings much-needed structure to a fragmented research area, evaluating the current state of forecasting methods and identifying the critical research directions required to improve the operational effectiveness of DR programs. Full article

Background: The aim of this bibliometric study was to evaluate publication trends in the most frequently cited clinical trials on the treatment of gingival recession, taking into account the augmentation materials used. Methods: A Web of Science search was performed among articles published by 30 September 2024. Two independent reviewers evaluated year of publication, journal, authorship country of authors, collaborative relationship, keywords, and the main domains. Results: The top one hundred most-cited clinical trials were published in the span of 26 years from 1993 to 2019, and the total citation counts varied from 44 to 284 (83.69 citations per paper). There was correlation between the time of publication and the number of citations. The articles were authored by 333 researchers representing twenty-two countries. Italy contributed the highest number of articles (n = 36), followed by the USA (n = 28) and Brazil (n = 17). International collaborations were predominantly observed between Italy, the USA, and Switzerland. The type of graft was the most cited field of research (34), followed by guided tissue regeneration (17) and enamel matrix derivative (13). Conclusions: The country that produced the highest number publications among the 100 most-cited clinical trials on gingival recession treatment was Italy. The use of connective tissue graft (CTG) and coronally advanced flap (CAF) was the most prominent trend. Future work should combine bibliometric mapping with critical quality appraisal and explore whether citation trends align with best available evidence. Full article

Background: Acute infections in children are prevalent and often lead to antibiotic overuse due to the lack of evidence-based alternative approaches. Phytotherapeutic, homeopathic treatments and bee products are frequently sought as alternative or adjunctive therapies. This scoping review aims to map the existing evidence on the efficacy and safety of these interventions in managing acute pediatric infections. Methods: A comprehensive literature search was conducted across multiple databases to identify studies assessing the use of phytotherapeutic, homeopathic remedies and bee products in children with acute infections. Gastrointestinal infections were not considered since the use of non-antibiotic treatments (probiotics) in these conditions has been widely addressed. Effectiveness: Phytotherapeutic agents and bee products demonstrated promising results in reducing symptom severity and duration in respiratory infections, whereas homeopathic data were limited and inconsistent. Regarding safety, both interventions were generally well-tolerated, with few adverse events reported. No studies or very limited evidence were available for other acute infections such as urinary, dermatological, osteoarticular and nervous system infections. Conclusions: Phytotherapeutic interventions and bee products, particularly in acute upper respiratory tract and acute bronchitis, show encouraging signals of efficacy and safety in pediatric populations. However, evidence for their use in other frequent childhood infections, such as otitis media, or gastrointestinal infections, is almost entirely lacking. In addition, the available literature on homeopathic remedies is scarce and methodologically inconsistent, preventing any firm conclusions. Well-designed, large-scale clinical trials focusing on these underexplored conditions are needed to clarify the potential role of phytotherapeutics and homeopathy in pediatric infectious diseases. Full article

Assessing the quality of groundwater, a primary source of water in many sectors, is of paramount importance. To this end, modeling the geospatial distribution of chemical contaminants in groundwater can be of great utility. Machine learning (ML) models are being increasingly used to overcome the shortcomings of conventional predictive techniques. We report here a systematic review of the nature and utility of various supervised and unsupervised ML models during the past two decades of machine learning groundwater hazard mapping (MLGHM). We identified and reviewed 284 relevant MLGHM journal articles that met our inclusion criteria. Firstly, trend analysis showed (i) an exponential increase in the number of MLGHM studies published between 2004 and 2025, with geographical distribution outlining Iran, India, the US, and China as the countries with the most extensively studied areas; (ii) nitrate as the most studied target, and groundwater chemicals as the most frequently considered category of predictive variables; (iii) that tree-based ML was the most popular model for feature selection; (iv) that supervised ML was far more favored than unsupervised ML (94% vs. 6% of models) with tree-based category—mostly random forest (RF)—as the most popular supervised ML. Secondly, compiling accuracy-based comparisons of ML models from the explored literature revealed that RF, deep learning, and ensembles (mostly meta-model ensembles and boosting ensembles) were frequently reported as the most accurate models. Thirdly, a critical evaluation of MLGHM models in terms of predictive accuracy, along with several other factors such as models’ computational efficiency and predictive power—which have often been overlooked in earlier review studies—resulted in considering the relative merits of commonly used MLGHM models. Accordingly, a flowchart was designed by integrating several MLGHM key criteria (i.e., accuracy, transparency, training speed, number of hyperparameters, intended scale of modeling, and required user’s expertise) to assist in informed model selection, recognising that the weighting of criteria for model selection may vary from problem to problem. Lastly, potential challenges that may arise during different stages of MLGHM efforts are discussed along with ideas for optimizing MLGHM models. Full article

Remote sensing technologies are increasingly integrated with autonomous robotic platforms to enhance data-driven decision-making in precision agriculture. Rather than replacing conventional platforms such as satellites or UAVs, autonomous ground robots complement them by enabling high-resolution, site-specific observations in real time, especially at the plant level. This review analyzes how remote sensing sensors—including multispectral, hyperspectral, LiDAR, and thermal—are deployed via robotic systems for specific agricultural tasks such as canopy mapping, weed identification, soil moisture monitoring, and precision spraying. Key benefits include higher spatial and temporal resolution, improved monitoring of under-canopy conditions, and enhanced task automation. However, the practical deployment of such systems is constrained by terrain complexity, power demands, and sensor calibration. The integration of artificial intelligence and IoT connectivity emerges as a critical enabler for responsive, scalable solutions. By focusing on how autonomous robots function as mobile sensor platforms, this article contributes to the understanding of their role within modern precision agriculture workflows. The findings support future development pathways aimed at increasing operational efficiency and sustainability across diverse crop systems. Full article