Search Results (8,848)

Background/Objectives: A high-performing blood biomarker (BBM) test for Alzheimer’s disease (AD) represents an accurate, accessible, and scalable tool to aid healthcare professionals (HCPs) evaluating patients presenting with signs or symptoms of mild cognitive impairment (MCI) or dementia. However, implementation of AD blood tests into clinical practice has not been extensively evaluated. The objective of this study was to assess the implementation of the multi-analyte PrecivityAD2™ blood test (C2N Diagnostics, LLC, St. Louis, MO, USA) into the clinical workflow of memory care clinics. Methods: A total of 8 HCPs (neurologists, geriatricians, geriatric psychiatrists) who served as site directors from 8 outpatient sites that evaluated 203 cognitively symptomatic patients were included in this sub-study of the real-world QUIP II Study (NCT06025877). Implementation of this blood test was assessed through surveying these HCPs using published frameworks including the Technology Acceptance Model, net promoter score, and forced choice preference questions. These assessments were analyzed using Wilcoxon signed-rank test, Fisher’s Exact test, and Wilcoxon signed-rank test, respectively. Results: HCPs reported acceptance scores that averaged 9.6 out of 10 (p < 0.0001, effect size 0.840): the test’s contribution to clinical decision-making as well as the ease of understanding test results received the highest ratings. The net promoter score was 75 (p < 0.0001), exceeding the typical benchmark of 30 reported as good levels of satisfaction in healthcare settings. The APS2 results and individual blood analyte results were rated with similar preference around their roles in HCP clinical decision-making. Conclusions: The results indicate early evidence of user acceptance and recognition by HCPs that this AD blood test can personalize the clinical care pathway for evaluating cognitively symptomatic patients. Full article

Background: Coronary collateral circulation (CCC) plays a crucial protective role in patients with chronic total occlusion (CTO), mitigating ischemia and improving long-term outcomes. However, the degree of collateral vessel development varies substantially among individuals. Systemic inflammatory and nutritional status may influence this variability. The Naples Prognostic Score (NPS) is a composite index reflecting these parameters, yet its relationship with CCC remains incompletely defined. Methods: We retrospectively analyzed 324 patients with angiographically confirmed CTO at Selçuk University Faculty of Medicine between 2014 and 2025. Coronary collaterals were graded using the Rentrop classification, and patients were categorized as having poor (grades 0–1) or good (grades 2–3) collaterals. The NPS was calculated using serum albumin, cholesterol, neutrophil-to-lymphocyte ratio, and lymphocyte-to-monocyte ratio. Baseline clinical and laboratory data were compared between groups. Univariate and multiple binary logistic regression analyses were performed to identify independent predictors of collateral development. Results: Of the 324 patients, 208 (64.2%) had poor and 116 (35.8%) had good collateral circulation. Patients with good collaterals had higher body mass index, HDL Cholesterol (HDL-C), and triglyceride levels, and significantly lower NPS values compared with those with poor collaterals (p < 0.05 for all). In multiple binary logistic regression analysis, HDL-C (OR 1.035; 95% CI 1.008–1.063; p = 0.011) and NPS (OR 0.226; 95% CI 0.130–0.393; p < 0.001) emerged as independent predictors of well-developed collaterals. Conclusions: Both NPS and HDL-C are independently associated with the degree of coronary collateral circulation in CTO patients. These findings highlight the interplay between systemic inflammation, nutritional status, lipid metabolism, and vascular adaptation. As simple and routinely available measures, NPS and HDL-C may serve as practical tools for risk stratification and identifying patients at risk of inadequate collateral formation. Prospective studies with functional assessments of collateral flow are warranted to confirm these associations and explore potential therapeutic interventions. Full article

This study investigates the development and application of the GDELT (Global Database of Events, Language, and Tone) news database. Through experiments, we conducted a quantitative statistical analysis of the GDELT event database to evaluate its practical characteristics. The results indicate that although the database achieves comprehensive coverage across all countries and regions and includes most major global media outlets, the accuracy rate of its key fields is only approximately 55%, with a data redundancy as high as 20%. Based on these findings, while the GDELT data demonstrates good coverage and data integrity, data correction and deduplication are recommended before its use in research contexts and industrial applications. Subsequently, a survey of the existing literature reveals that current studies using GDELT primarily focused on event-related metrics, such as event quantity, tone, and GoldsteinScale, for application in international relations analysis, crisis event prediction, policy effectiveness testing, and public opinion impact analysis. Nevertheless, news constitutes a fundamental channel of information dissemination in media networks, and the propagation of news events through these networks represents a critical area of study for information recommendation, public opinion guidance, and crisis intervention. Existing research has employed the Event, GKG, and Mentions tables to construct cross-national news flow network models. However, the informational correlations across different data table fields have not been fully leveraged in preliminary data selection, leading to substantial computational overhead. To advance research in this field, this study employs chained list queries on the Event and Mentions tables within GDELT. Using social network analysis, we constructed a media co-occurrence network of event reports, through which core hubs and associative relationships within the event dissemination network are identified. Full article

Sustainability concerns and rising consumer environmental awareness (CEA) have fundamentally reshaped competitive dynamics in modern supply chains. This study examines the influence of CEA on pricing and environmental effort competition between store brand (SB) and national brand (NB) products in a two-stage supply chain with one manufacturer and one retailer. We develop a mathematical model to evaluate strategic interactions under three power structures: Manufacturer Stackelberg (MS), Retailer Stackelberg (RS), and Vertical Nash (VN), considering two environmental investment scenarios: NB-only investment and bilateral SB-NB investment. Our findings indicate that (i) when only NB products invest environmentally, CEA increases environmental effort levels, wholesale prices, and retail prices for both brands, expanding total channel value rather than merely redistributing profits; (ii) CEA and channel competition on jointly determine optimal channel power structure, with MS dominating in differentiated markets with low CEA while RS yields superior outcomes under high competition and high CEA; (iii) retailers consistently achieve maximum profits under VN structure through balanced negotiation positions; and (iv) bilateral environmental investment causes price convergence across structures, shifting competitive focus from governance to operational excellence. By integrating environmental investment, channel power structure, and channel competition into a unified framework, this study offers managers practical decision tools for selecting optimal channel structures based on observable market conditions. Furthermore, it demonstrates how grocery retail chains and consumer goods manufacturers can transform environmental initiatives from compliance costs into value creation mechanisms that enhance both profitability and sustainability. Full article

With the integration of the global economy and the rapid development of port logistics, the port dangerous goods transportation system faces complex risk-coupling problems, and the probability of accidents keeps climbing. However, the existing research on the system risk-coupling mechanism and dynamic control mechanism is still insufficient, and there is an urgent need to construct a scientific risk analysis and control model. This study takes the port dangerous goods transportation system as the object, based on the four-factor framework of “personnel-machine-environment-management,” uses the N-K model to quantify the degree of risk coupling, analyzes the dynamic evolution mechanism of risk under the action of a single factor, and uses Dufferin’s oscillation and bifurcation response equation to reveal the interaction between the system’s internal defenses and the external influences. It is found that the coupled risk value of personnel–machine factors is the highest, and the sudden change in system state is characterized by a sudden jump and lag. The system stability can be significantly improved by enhancing internal damping control and optimizing external excitation regulation. This study provides a quantitative tool for the risk assessment of dangerous goods transportation in ports and theoretical support for the development of the “damping-excitation” synergistic control strategy, which is of great practical significance for the improvement of the port safety management system. Full article

Accurate recognition of estrus behavior in sows is of great importance for achieving scientific breeding management, improving reproductive efficiency, and reducing labor costs in modern pig farms. However, due to the evident spatiotemporal continuity, stage-specific changes, and ambiguous category boundaries of estrus behaviors, traditional methods based on static images or manual observation suffer from low efficiency and high misjudgment rates in practical applications. To address these issues, this study follows a video-based behavior recognition approach and designs three deep learning model structures: (Convolutional Neural Network combined with Long Short-Term Memory) CNN + LSTM, (Three-Dimensional Convolutional Neural Network) 3D-CNN, and (Convolutional Neural Network combined with Temporal Convolutional Network) CNN + TCN, aiming to achieve high-precision recognition and classification of four key behaviors (SOB, SOC, SOS, SOW) during the estrus process in sows. In terms of data processing, a sliding window strategy was adopted to slice the annotated video sequences, constructing image sequence samples with uniform length. The training, validation, and test sets were divided in a 6:2:2 ratio, ensuring balanced distribution of behavior categories. During model training and evaluation, a systematic comparative analysis was conducted from multiple aspects, including loss function variation (Loss), accuracy, precision, recall, F1-score, confusion matrix, and ROC-AUC curves. Experimental results show that the CNN + TCN model performed best overall, with validation accuracy exceeding 0.98, F1-score approaching 1.0, and an average AUC value of 0.9988, demonstrating excellent recognition accuracy and generalization ability. The 3D-CNN model performed well in recognizing short-term dynamic behaviors (such as SOC), achieving a validation F1-score of 0.91 and an AUC of 0.770, making it suitable for high-frequency, short-duration behavior recognition. The CNN + LSTM model exhibited good robustness in handling long-duration static behaviors (such as SOB and SOS), with a validation accuracy of 0.99 and an AUC of 0.9965. In addition, this study further developed an intelligent recognition system with front-end visualization, result feedback, and user interaction functions, enabling local deployment and real-time application of the model in farming environments, thus providing practical technical support for the digitalization and intelligentization of reproductive management in large-scale pig farms. Full article

Allogeneic chondrocyte therapies present an attractive alternative to existing autologous therapies for the repair of cartilage defects, enabling the selection of optimal donor cells and streamlined manufacturing processes. This study investigates the potential of juvenile chondrocytes derived from human infantile (aged 0–4 y) polydactyly digits and the iliac apophysis for cartilage repair using Good Manufacturing Practice bioreactor expansion. Iliac apophysis (n = 4) and polydactyly tissues (n = 4) were assessed histologically. Chondrocytes were isolated enzymatically and cultured using standard tissue culture plastic (TCP) methodology. Upon sufficient cell expansion, chondrocytes were seeded into the Quantum^® bioreactor system or onto TCP (±vitronectin coating). The manufactured chondrocytes growth rates, total cell yields, chondrogenic pellet forming capacity (GAG/DNA, histology), immunoprofiles (flow cytometry) and gene expression (RT-qPCR) were assessed. Equivalent chondrocyte numbers were isolated from polydactyly and iliac apophysis donors per wet weight of tissue. Quantum^®-expanded chondrocytes from both sources yielded comparable cell numbers; however, growth was slowed in the Quantum^® compared to TCP. Polydactyly and iliac apophysis-derived chondrocytes expressed chondrocyte cell surface markers (CD166, CD44, CD151, SOX9) and formed chondrogenic pellets. Quantum^® bioreactor expansion did not alter, gene expression or capacity to form glycosaminoglycans (GAGs (normalised to DNA content)) compared to matched TCP expansion. Juvenile cartilage donors are a promising chondrocyte source for the development of an allogeneic therapy. This novel study expanding juvenile chondrocytes in the Quantum^® GMP-compliant bioreactor suggests that culture conditions may need modification to improve growth, whilst retaining cartilage forming capacity. Full article

Foodborne diseases (FBDs) represent significant public health concerns as they are conditions associated with deficient manufacturing practices. They comprise important diseases with acute or chronic courses, frequently occurring in outbreak form and associated with significant gastrointestinal disorders. FBDs are related to infrastructure and organizational issues in urban centers, such that contamination in food processing facilities, lack of access to basic sanitation, and social and financial vulnerability are some of the factors that favor their occurrence and the demand for health services. Among the agents associated with FBDs is Listeria sp., especially Listeria monocytogenes (L. monocytogenes). The objective of this article is to characterize L. monocytogenes and its potential impact on One Health, given its importance as a significant foodborne pathogen. A thorough scientific literature search was conducted to obtain information on the subject, aiming to assist in the verification and presentation of evidence. L. monocytogenes is a pathogen with specific characteristics that ensure its adhesion, adaptation, growth, and survival on various surfaces, such as biofilm formation ability and thermotolerance. Several diagnostic methods are available for detection of the agent, including enrichment media, molecular techniques, and subtyping evaluation. Its control represents a significant challenge, with critical implications due to bacterial perpetuation characteristics and the implementation/monitoring of sanitization programs and commercialization of animal-derived products (POAO). Thus, vulnerable and susceptible populations are more exposed to foodborne pathogens due to health-related determinants, such as inadequate sanitation, poor food safety control, and insufficient personal hygiene. The pathogen’s persistence and difficulty of control represent a significant public One Health threat. Full article

Data-driven modeling methods have been preliminarily applied in the development of tight-gas reservoirs, demonstrating unique advantages in post-fracturing productivity prediction. However, most of the established predictive models are “black-box” models, which provide productivity predictions based on a set of input parameters without revealing the internal prediction mechanisms. This lack of transparency reduces the credibility and practical utility of such models. To address the challenges of poor performance and low trustworthiness of “black-box” machine learning models, this study explores a data-driven approach to “black-box” predictive modeling by integrating ensemble learning with interpretability methods. The results indicate the following: The post-fracturing productivity prediction model for tight-gas reservoirs developed in this study, based on ensemble learning, achieves a goodness of fit of 0.923, representing a 26.09% improvement compared to the best-performing individual machine learning model. The stacking ensemble model predicts post-fracturing productivity for horizontal wells more accurately and effectively mitigates the prediction biases of individual machine learning models. An interpretability method for the “black-box” ensemble learning-based productivity prediction model was established, revealing the ranked importance of factors influencing post-fracturing productivity: reservoir properties, controllable operational parameters, and rock mechanics. This ranking aligns with the results of orthogonal experiments from mechanism-driven numerical models, providing mutual validation and enhancing the credibility of the ensemble learning-based productivity prediction model. In conclusion, this study integrates mechanistic numerical models and data-driven models to explore the influence of various factors on post-fracturing productivity. The cross-validation of results from both approaches underscores the reliability of the findings, offering theoretical and methodological support for the design of fracturing schemes and the iterative advancement of fracturing technologies in tight-gas reservoirs. Full article

This study evaluated the feasibility of including a computerized dynamic test of analogical reasoning in standard neuropsychological assessments in a heterogeneous psychiatric population. The participants were 40 adult patients (M_age = 33.15 ± 12.27, range 19–68; 60% male) enrolled in specialized mental health and forensic care programs in The Netherlands, who were randomly assigned into either a training, a passive, or a control group. A pretest–training–posttest paradigm was used for the training group, and the dynamic test consisted of 26 items of the A:B::C:? type. In terms of practical use, it was found that the administration time varied largely, and 22% of the data was lost due to drop out or technical malfunctions. Test–retest reliability was acceptable for the training group (r = 0.61) and good for the practice and control groups (resp. r = 0.88 and 0.80). A statistical trend was observed for the training vs. practice group (Z = −1.598, p = 0.055), but not for the training vs. control group (Z = −0.839, p = 0.201). It was concluded that an indication of training effectiveness was found; however, in this clinical sample, the applicability of the current form of the dynamic test is still limited. Several modification options are discussed. Full article

Background/Objectives: Executive functioning (EF) and prospective memory (PM) difficulties have been documented in adults with developmental dyslexia. However, research has tended to focus on university students with a formal diagnosis, overlooking adults who may experience symptoms of dyslexia without ever being formally diagnosed. The present online study aimed to investigate the effects of dyslexia traits on EF and PM in a larger, community-based sample by prioritising the use of self-report measures that include and differentiate between underexplored aspects of EF and PM in the dyslexia literature. Methods: One hundred and eighty adult volunteers of a wide range of ages were included in the final analysis. Participants completed four self-report questionnaires with good pedigrees assessing dyslexia traits and ADHD symptomatology, as well as everyday experiences of five distinct EFs, PM and PM strategies. Results: Hierarchical regression analyses revealed that, after controlling for age and ADHD symptomatology, more self-reported dyslexia traits were associated with more frequent EF problems overall and lower confidence in PM Abilities. Elevated dyslexia traits were significantly associated with self-reports of more difficulties with time-management, organisation and self-restraint, but not with more difficulties with motivation and regulation of emotion. No significant associations were found between dyslexia traits and the self-reported use of PM-enhancing strategies. Conclusions: The findings are considered in the light of potential metacognition deficits and theoretical and practical implications are discussed. Full article

Background: Perioperative Cardiac Arrest (POCA) is a rare but catastrophic event with persistently low survival rates. Existing prediction models often fail to capture the perioperative context or predict short-term outcomes. This study aimed to develop and internally validate the POTEC (Platelet count, Oxygen saturation, Time of cardiopulmonary resuscitation (CPR), Elective surgery, and initial end-tidal carbon dioxide (ETCO₂) Score, a simple clinical tool for predicting 24-h survival following perioperative CPR. Methods: We conducted a retrospective cohort study of adult patients (≥18 years) who experienced POCA during or within two hours after non-cardiac surgery under anesthesia at a tertiary university hospital between 2010 and 2023. Multivariable logistic regression was used to identify independent predictors of 24-h survival. The final model’s coefficients were used to construct the POTEC Score, which was internally validated using bootstrapping (1000 replications). Results: Of 321 eligible patients, 65 (20.25%) survived at 24-h. Five variables were independently associated with 24-h survival and included in the POTEC score: preoperative platelet count 100 × 10⁹/L, preoperative oxygen saturation of ≥90% on room air upon arrival in the operating room, CPR duration ≤30 min, elective surgery, and initial end-tidal CO₂ between 35 and 45 mmHg. The score demonstrated good discrimination (AuROC = 0.788, 95% CI: 0.73–0.85) and calibration (Hosmer–Lemeshow p = 0.535). A score of 4 points or higher was associated with significantly higher odds of 24-h survival (adjusted OR = 2.78, 95% CI: 2.05–3.79). Model optimism was minimal (0.009) after bootstrapping. Conclusions: The POTEC Score is a clinically practical tool for early risk stratification in patients undergoing perioperative CPR. Its integration into perioperative workflows may aid in timely decision-making and resource prioritization during critical postoperative care. Full article

The growing global population and rising organic waste generation necessitate innovative and sustainable waste management solutions. This study investigated the potential of black soldier fly larvae (BSFL) as a bioconversion agent for agro-industrial and community organic waste, with an emphasis on optimizing substrate composition for enhanced treatment performance of BSFL. Six rearing substrates were formulated by mixing brewery waste, vegetable and fruit waste, food waste, and sugar filter cake in varying ratios. The performance of BSFL was assessed using five key performance indicators, and an overall performance score was derived to compare substrate suitability across three dimensions: biomass yield, waste reduction, and larval development time. The results revealed that BSFL survival exceeded 97% for all substrates. The highest waste reduction rate of 67.52% was achieved with a 50:50 mixture of brewery waste and food waste. This mixture also attained an overall performance score of 0.77 out of 1, classified as “good”. In contrast, sugar filter cake proved unsuitable for BSFL rearing due to its low nutritional value. These findings offer practical guidelines for selecting optimal waste mixtures to improve the efficiency of BSFL-based waste management. Full article

Caregiver self-regulation may be a critical component of caregivers’ effective delivery of caregiver-mediated interventions (CMIs). CMIs are a highly evidence-based group of interventions that target a broad range of challenges, including social communication, emotion regulation, and externalizing behaviors, for autistic and neurotypical children. CMIs teach caregivers to be “coaches” to help their children learn and practice skills in daily life. However, being a good “coach” likely requires caregivers to optimally self-regulate their emotions, thoughts, and behaviors when working with their children in moments that are often emotionally heightened. Caregiver self-regulation is a set of skills that promote parenting autonomy and confidence: self-sufficiency, self-efficacy, self-management, personal agency, and problem solving. This conceptual paper will briefly discuss the literature on the role of caregiver self-regulation in CMIs and argue that future implementation research on CMIs should measure caregiver self-regulation because, in line with recent expansion of the theory of planned behavior, caregiver self-regulation may predict more effective implementation of CMIs. We also argue, in line with CFIR 2.0, that supporting caregiver self-regulation could ultimately improve the implementation of CMIs with regard to each implementation outcome in the Implementation Outcomes Framework. For example, enhancing caregiver self-regulation may improve CMI appropriateness (by increasing alignment with each caregiver’s values and culture), adoption (by increasing engagement to finish the full CMI protocol), and even CMI sustainability (by increasing caregivers’ ability to problem-solve and generalize to new child challenges independently, freeing up provider time to work with new caregivers and allowing the agency to provide the CMI for a reduced relative cost). Should future research demonstrate that caregiver self-regulation is an implementation determinant, future implementation strategies may need to include support for caregiver self-regulation, because it may explain or enhance the implementation of CMIs across early intervention and community mental health systems. Full article

Practicing medical research based on the best evidence is gaining increased value and popularity among most medical societies in the current era. Good clinical practice (GCP) is internationally recognized as the scientific and ethical standard for the design, conduct, performance, auditing, recording, analysis, and reporting of clinical trials involving human subjects. GCP ensures the accuracy and credibility of trial while safeguarding the rights, integrity, and confidentiality of participants. Adherence to GCP facilitates the generation of high-quality studies that can be incorporated in Evidence-Based Medicine (EBM). The clinical practice of EBM seeks to integrate robust medical literature into daily medical practice. This process involves systematically searching for high-quality evidence, critically appraising the retrieved literature, applying sound clinical principles and finally evaluating the efficacy of the chosen approach. Although EBM has been evaluated in many resource settings, it has not been addressed sufficiently in the field of Pediatrics and more specifically in indigenous populations. In this review, we briefly explain the EBM approach and its applications in Pediatrics, in order to help physicians care for young subjects more efficiently by integrating the best available information into their routine clinical practice. Also, the basic good practice principles for conducting clinical trials in children and adolescents are highlighted, emphasizing the importance of applying high ethical principles in this vulnerable population. Full article