Search Results (268)

Background: Refractory ventricular fibrillation (RVF) is a life-threatening condition characterized by the persistence of ventricular fibrillation despite multiple defibrillation attempts. It represents a critical challenge in out-of-hospital cardiac arrest management, with poor survival outcomes and limited guidance from current resuscitation guidelines. In recent years, alternative defibrillation strategies (ADSs), including dual sequential external defibrillation (DSED) and vector change defibrillation (VCD), have emerged as potential interventions to improve defibrillation success and patient outcomes. However, their clinical utility remains debated due to heterogeneous evidence and limited high-quality data. Methods: This narrative review explores the current landscape of ADSs in patients with RVF. MEDLINE, Google Scholar, the World Health Organization, LitCovid NLM, EMBASE, CINAHL Plus, and the Cochrane Library were examined from their inception to April 2025. Results: The available literature is dominated by retrospective studies and case series, with only one randomized controlled trial (DOSE-VF). This trial demonstrated improved survival to hospital discharge with ADSs compared to standard defibrillation. DSED was associated with higher rates of return of spontaneous circulation and favorable neurological outcomes. However, subsequent meta-analyses have produced inconsistent results, largely due to the heterogeneity of the included studies. The absence of sex-, gender-, and ethnicity-specific analyses further limits the generalizability of the findings. In addition, practical barriers, such as equipment availability, pose significant challenges to implementation. Conclusions: ADSs represent a promising yet still-evolving approach to the management of RVF, with DSED showing the most consistent signal of benefit. Further high-quality research is required to enhance generalizability and generate more definitive, high-level evidence. Full article

Background: Early identification of sepsis is critical for improving outcomes, particularly in low-resource emergency settings. In Thai community hospitals, where physicians may not always be available, triage is often nurse-led. Selecting accurate and practical sepsis screening tools is essential not only for timely clinical decision-making but also for timely diagnosis and promoting appropriate antibiotic use. Methods: This cross-sectional study analyzed 475 adult patients with suspected sepsis who presented to the emergency department of a Thai community hospital, using retrospective data from January 2021 to December 2022. Six screening tools were evaluated: Systemic Inflammatory Response Syndrome (SIRS), Quick Sequential Organ Failure Assessment (qSOFA), Modified Early Warning Score (MEWS), National Early Warning Score (NEWS), National Early Warning Score version 2 (NEWS2), and Search Out Severity (SOS). Diagnostic accuracy was assessed using International Classification of Diseases, Tenth Revision (ICD-10) codes as the reference standard. Performance metrics included sensitivity, specificity, predictive values, likelihood ratios, and the area under the receiver operating characteristic (AUROC) curve, all reported with 95% confidence intervals. Results: SIRS had the highest sensitivity (84%), while qSOFA demonstrated the highest specificity (91%). NEWS2, NEWS, and MEWS showed moderate and balanced diagnostic accuracy. SOS also demonstrated moderate accuracy. Conclusions: A two-step screening approach—using SIRS for initial triage followed by NEWS2 for confirmation—is recommended. This strategy enhances nurse-led screening and optimizes limited resources in emergency care. Early sepsis detection through accurate screening tools constitutes a feasible public health intervention to support appropriate antibiotic use and mitigate antimicrobial resistance, especially in resource-limited community hospital settings. Full article

Background/Objectives: Tongue thrust (TT) occurs when abnormal tongue movements cause anterior tongue placement with pressure and contact against or between the teeth, potentially affecting the oral phase of swallowing, impacting eating, breathing and speaking. There is limited literature on the diagnostic and treatment approaches for TT, as well as involvement of health practitioners in its management. This study aims to examine the current knowledge and practices related to TT diagnosis and treatment among health professionals in Australia. Methods: A two-phase explanatory sequential mixed methods approach was adopted, comprising an online survey that collected participants’ demographic information and details on assessment, diagnosis, management, referral practices, and relevant experience and training. Phase one involved 47 health professionals from various disciplines in Australia who completed an online survey in its entirety. Phase two included in-depth interviews with seven speech-language pathologists (SLPs) to gain further insights into their experiences in managing TT. Survey data were analysed descriptively, and interview data was analysed thematically. Results: Most participants diagnosed TT using clinical assessments, such as general observation and oral motor examinations. Treatment approaches commonly included orofacial myofunctional therapy and the use of myofunctional devices. Interviews with SLPs identified four key themes: tongue thrust as a symptom rather than a diagnosis, facilitators to effective treatment, multidisciplinary approaches to management, and training and education gaps in clinical practice. Conclusions: This study provides valuable insights into how TT is identified, assessed, diagnosed, and managed by health professionals in Australia. It highlights the perspectives of SLPs on treatment approaches, as well as their views on the availability and adequacy of training and education in this field. The findings suggest the need for a broader understanding of TT management, emphasising the importance of multidisciplinary collaboration and professional development. These insights are globally relevant, as they stress the shared challenges and the value of international collaboration in improving TT diagnosis and treatment practices. Full article

Objectives: The early detection of low bone mineral density (BMD) is essential for preventing osteoporosis and related complications. While dual-energy X-ray absorptiometry (DXA) remains the gold standard for diagnosis, its cost and limited availability restrict its use in large-scale screening. This study investigated whether raw bioelectrical impedance analysis (BIA) data combined with explainable machine learning (ML) models could accurately classify osteopenia in women aged 40 to 55. Methods: In a cross-sectional design, 138 women underwent same-day BIA and DXA assessments. Participants were categorized as osteopenic (T-score between −1.0 and −2.5; n = 33) or normal (T-score ≥ −1.0) based on DXA results. Overall, 24.1% of the sample were classified as osteopenic, and 32.85% were postmenopausal. Raw BIA outputs were used as input features, including impedance values, phase angles, and segmental tissue parameters. A sequential forward feature selection (SFFS) algorithm was employed to optimize input dimensionality. Four ML classifiers were trained using stratified five-fold cross-validation, and SHapley Additive exPlanations (SHAP) were applied to interpret feature contributions. Results: The neural network (NN) model achieved the highest classification accuracy (92.12%) using 34 selected features, including raw impedance measurements, derived body composition indices such as regional lean mass estimates and the edema index, as well as a limited number of categorical variables, including self-reported physical activity status. SHAP analysis identified muscle mass indices and fluid distribution metrics, features previously associated with bone health, as the most influential predictors in the current model. Other classifiers performed comparably but with lower precision or interpretability. Conclusions: ML models based on raw BIA data can classify osteopenia with high accuracy and clinical transparency. This approach provides a cost-effective and interpretable alternative for the early identification of individuals at risk for low BMD in resource-limited or primary care settings. Full article

Habitat quality functions as a critical metric for evaluating regional ecological health and the capacity of ecosystem services. Understanding its temporal dynamics is critical for advancing ecological civilization sustainability. Focusing on the Tacheng region, this study analyzes the evolution characteristics of land use based on long-term sequential land use data from 2003 to 2023. By coupling the PLUS and InVEST models, it predicts land use change trends under three distinct scenarios for the year 2033 and assesses the spatiotemporal evolution characteristics of habitat quality in the Tacheng region from 2003 to 2033. Findings reveal: (1) The land use types in the Tacheng region are dominated by grassland and unutilized land. During 2003–2023, the area of grassland and water showed a decreasing trend, while the areas of cultivated land and unutilized land significantly increased. Among them, NDVI was identified as the primary driver influencing the expansion of cultivated land, grassland, and unutilized land in the Tacheng region, addressing the gap in quantitative contribution analysis of specific drivers in arid region studies. (2) Overall, habitat quality in the Tacheng region significantly deteriorated during 2003–2023, with areas of high habitat quality continuously decreasing and transitioning to medium and relatively low habitat quality zones. This degradation is primarily attributed to the unidirectional conversion of grassland and water into cultivated land and unutilized land. (3) Under different scenario simulations, land use and habitat quality in the Tacheng region exhibit marked differences, with habitat quality showing significant improvement, particularly under the ecological protection scenario. Compared to existing research, this study pioneers the coupling of PLUS and InVEST models in the typical arid region of the Tacheng region, implements localization of model parameters, quantifies future evolution trends of land use and habitat quality under multiple scenarios, and reveals core drivers of land use change in arid regions. This work addresses the research gap regarding habitat quality simulation and driving mechanisms in the Central Asian arid-semiarid transition zone. Full article

This study investigates the feasibility of using GPS data and frequency of COVID-19-related blog words to forecast new infection trends through a linear regression analysis. By employing time series’ trend decomposition and Spearman’s rank correlation, we identify and select a set of significant variables from the GPS and blog data to construct two models: a fixed-period model and a sequential adaptive model that updates with each new wave of infections. Our findings reveal that the adaptive model more effectively captures long-term trends, achieving approximately 90% accuracy in forecasting infection rates seven days in advance. Despite challenges in forecasting exact values, this research demonstrates that combining GPS and blog data through a dynamic, wave-based learning model offers a promising direction for enhancing the forecasting accuracy of COVID-19 spread. This approach has significant implications for public health preparedness. Full article

The COVID-19 pandemic is a catastrophic event highlighting numerous health disparities. The social vulnerability index (SVI) has been widely utilized in COVID-19 research to assess vulnerable communities and to examine how social determinants influence various COVID-19 outcomes. This population-based study aims to determine whether COVID-19 testing and vaccination rates mediate the relationship between the SVI and COVID-19 prevalence. Mediation analysis was conducted using data from 535 census tracts in Clark County, Nevada. Findings indicate that COVID-19 testing rates were lower in areas with high SVI scores, potentially leading to more undetected cases. Moreover, COVID-19 testing, full vaccination, and follow-up vaccination rates significantly mediated the relationship between SVI and COVID-19 prevalence. These results suggest that greater location-based social vulnerability is associated with a sequential pathway of reduced testing and vaccination rates, contributing to underreported COVID-19 cases. Full article

Background: The rapid integration of artificial intelligence (AI) technologies into healthcare systems presents new opportunities and challenges, particularly regarding legal and ethical implications. In Saudi Arabia, the lack of legal awareness could hinder safe implementation of AI tools. Methods: A sequential explanatory mixed-methods design was employed. In Phase One, a structured electronic survey was administered to 357 clinicians across public and private healthcare institutions in Saudi Arabia, assessing legal awareness, liability concerns, data privacy, and trust in AI. In Phase Two, a qualitative expert panel involving health law specialists, digital health advisors, and clinicians was conducted to interpret survey findings and identify key regulatory needs. Results: Only 7% of clinicians reported high familiarity with AI legal implications, and 89% had no formal legal training. Confidence in AI compliance with data laws was low (mean score: 1.40/3). Statistically significant associations were found between professional role and legal familiarity (χ² = 18.6, p < 0.01), and between legal training and confidence in AI compliance (t ≈ 6.1, p < 0.001). Qualitative findings highlighted six core legal barriers including lack of training, unclear liability, and gaps in regulatory alignment with national laws like the Personal Data Protection Law (PDPL). Conclusions: The study highlights a major gap in legal readiness among Saudi clinicians, which affects patient safety, liability, and trust in AI. Although clinicians are open to using AI, unclear regulations pose barriers to safe adoption. Experts call for national legal standards, mandatory training, and informed consent protocols. A clear legal framework and clinician education are crucial for the ethical and effective use of AI in healthcare. Full article

Recent advances in sensor technology, data acquisition, and signal processing have enabled the development of data-driven structural health monitoring (SHM) strategies, offering a powerful alternative or complement to traditional model-based approaches. These approaches rely on damage-sensitive features (DSFs) extracted from vibration measurements. This study introduces an innovative, unsupervised learning framework leveraging transmissibility functions (TFs) as DSFs due to their local sensitivity to changes in dynamic behavior and their ability to operate without requiring input excitation measurements—an advantage in civil engineering applications where such data are often difficult to obtain. The novelty lies in the use of sequential sensor pairings based on structural connectivity to construct TFs that maximize damage sensitivity, combined with one-class classification algorithms for automatic damage detection and a damage index for spatial localization within sensor resolution. The method is evaluated through numerical simulations with noise-contaminated data and experimental tests on a masonry arch bridge model subjected to progressive damage. The numerical study shows detection accuracy above 90% with one-class support vector machine (OCSVM) and correct localization across all damage scenarios. Experimental findings further confirm the proposed approach’s localization capability, especially as damage severity increases, aligning well with observed damage progression. These results demonstrate the method’s practical potential for real-world SHM applications. Full article

Among transgender binary and nonbinary/queer (TNBQ) individuals, perceived stigma has been documented to be significantly associated with health disparities that diminish overall quality of life. The present study examined the serial mediating roles of patient–provider relationship quality and perceived barriers to care in the association between perceived stigma and quality of life among TNBQ individuals residing in Italy. Data were collected from 132 TNBQ participants aged 18–60 years (M = 28.52, SD = 8.57) through an online survey assessing perceived stigma, patient–provider relationship quality, perceived barriers to care, and quality of life. A serial mediation model was analyzed using Model 6 of the SPSS Macro Process, version 29, and separately applied to two subgroups of TNBQ participants (i.e., binary and nonbinary) to detect potential differences. Findings indicated that in both groups (i.e., binary and nonbinary populations), when considered independently, only perceived barriers to care—but not patient–provider relationship quality—mediated the relationship between perceived stigma and quality of life. A serial mediation effect was also observed, wherein the relationship between perceived stigma and quality of life was mediated sequentially through patient–provider relationship quality and barriers to care, but only for the binary group. These findings hold significant clinical relevance, as improving the perceived quality of patient–provider relationships may help reduce perceived barriers to healthcare access. In turn, this may attenuate the detrimental effects of perceived stigma on the quality of life among TNBQ individuals. Full article

Estimating Remaining Useful Life (RUL) is crucial in modern Prognostic and Health Management (PHM) systems providing valuable information for planning the maintenance strategy of critical components in complex systems such as aircraft engines. Deep Learning (DL) models have shown great performance in the accurate prediction of RUL, building hierarchical representations by the stacking of multiple explicit neural layers. In the current research paper, we follow a different approach presenting a Deep Equilibrium Model (DEM) that effectively captures the spatial and temporal information of the sequential sensor. The DEM, which incorporates convolutional layers and a novel dual-input interconnection mechanism to capture sensor information effectively, estimates the degradation representation implicitly as the equilibrium solution of an equation, rather than explicitly computing it through multiple layer passes. The convergence representation of the DEM is estimated by a fixed-point equation solver while the computation of the gradients in the backward pass is made using the Implicit Function Theorem (IFT). The Monte Carlo Dropout (MCD) technique under calibration is the final key component of the framework that enhances regularization and performance providing a confidence interval for each prediction, contributing to a more robust and reliable outcome. Simulation experiments on the widely used NASA Turbofan Jet Engine Data Set show consistent improvements, with the proposed framework offering a competitive alternative for RUL prediction under diverse conditions. Full article

Drawing on Antonovsky’s salutogenic model, this study investigated how stressful life events relate to university students’ sense of coherence (SOC), focusing on the potential mediating roles of coping style and general self-efficacy, and the moderating role of stress mindset. An analysis of data collected from 2454 Chinese college students (63.6% males, 36.4% females) revealed that stressful life events negatively predicted SOC, with coping style and general self-efficacy significantly sequentially mediating this relationship. Furthermore, stress mindset moderated the relationship between stressful life events and coping style, such that a more positive mindset was associated with more adaptive coping under stress. These findings support the dual-pathway structure of the salutogenic model by illustrating both behavioral and perceptual mechanisms. Importantly, they also underscore the idea that stress, when cognitively reappraised and effectively managed, may contribute to the development of SOC—rather than simply undermining it. This highlights the potential value of stress itself within salutogenic processes. The study offers theoretical insights and preliminary directions for strength-based mental health promotion in higher education settings. Full article

There are two types of noninvasive biomarkers of human embryo developmental potential: those based on a direct assessment of embryo morphology over time and those using spent media after embryo in vitro culture as source of information. Both are derived from previously acquired knowledge on different aspects of pre-implantation embryo development. These aspects include embryo morphology and kinetics, chromosomal ploidy status, metabolism, and embryonic gene transcription, translation, and expression. As to the direct assessment of morphology and kinetics, pertinent data can be obtained by analyzing sequential microscopic images of in vitro cultured embryos. Spent media can serve a source of genomic, metabolomic, transcriptomic and proteomic markers. Methods used in the early pioneering studies, such as microscopy, fluorescence in situ hybridization, autoradiography, electrophoresis and immunoblotting, or enzyme-linked immunosorbent assay, are too subjective, invasive, and/or time-consuming. As such, they are unsuitable for the current in vitro fertilization (IVF) practice, which needs objective, rapid, and noninvasive selection of the best embryo for uterine transfer or cryopreservation. This has been made possible by the use of high-throughput techniques such as time-lapse (for direct embryo evaluation), next-generation sequencing, quantitative real-time polymerase chain reaction, high-performance liquid chromatography, nanoparticle tracking analysis, flow cytometry, mass spectroscopy, Raman spectroscopy, near-infrared spectroscopy, and nuclear magnetic resonance spectroscopy (for spent culture media analysis). In this review, individual markers are presented systematically, with each marker’s history and current status, including available methodologies, strengths, and limitations, so as to make the essential information accessible to all health professionals, even those whose expertise in the matter is limited. Full article

Depression and anxiety are common comorbidities of stroke. Research has shown that about 30% of stroke survivors develop depression and about 20% develop anxiety. Stroke survivors with such adverse mental outcomes are often attributed to poorer health outcomes, such as higher mortality rates. The objective of this study is to use deep learning (DL) methods to predict the risk of a stroke survivor experiencing post-stroke depression and/or post-stroke anxiety, which is collectively known as post-stroke adverse mental outcomes (PSAMO). This study studied 179 patients with stroke, who were further classified into PSAMO versus no PSAMO group based on the results of validated depression and anxiety questionnaires, which are the industry’s gold standard. This study collected demographic and sociological data, quality of life scores, stroke-related information, medical and medication history, and comorbidities. In addition, sequential data such as daily lab results taken seven consecutive days after admission are also collected. The combination of using DL algorithms, such as multi-layer perceptron (MLP) and long short-term memory (LSTM), which can process complex patterns in the data, and the inclusion of new data types, such as sequential data, helped to improve model performance. Accurate prediction of PSAMO helps clinicians make early intervention care plans and potentially reduce the incidence of PSAMO. Full article

Background/Objectives: Gestational weight gain (GWG) is a critical determinant of pregnancy outcomes; however, studies on factors contributing to suboptimal GWG in developing countries, including Malaysia, remain limited. Methods: This study employed an explanatory sequential mixed-methods design, with the quantitative phase conducted between January and March 2020, followed by the qualitative phase from July 2020 to March 2021 in Selangor. The qualitative phase aimed to explain the factors influencing suboptimal (inadequate and excessive) GWG identified in the quantitative phase. Inclusion criteria included Malaysian women aged 18 and above who had suboptimal GWG (either inadequate or excessive) from the quantitative phase. Exclusion criteria included women who refused participation. Of the 475 participants from the quantitative phase, 20 with suboptimal GWG were purposively selected for in-depth telephone interviews using a semi-structured interview protocol. Data were analysed using thematic analysis. Results: Three key themes emerged: (1) the impact of pre-pregnancy overweight and obesity, shaped by unhealthy lifestyles, social influences, and limited access to nutritious food and physical activity; (2) the management of diabetes during pregnancy, contributing to inadequate GWG due to psychological responses, restrictive behaviours, and barriers to dietary guidance; and (3) financial constraints in middle- and low-income households, leading to income vulnerability, financial crises, and food insecurity. Conclusions: This finding highlights the urgent need for targeted interventions to optimize GWG, emphasizing pre-pregnancy health optimization, enhanced diabetes management, and strategies to mitigate financial constraints and food insecurity among pregnant women. Full article