Browse Articles

Study on the key factors of public participation in low carbon city construction and willingness to pay

Measurement accuracy and cutoffs for predicting primary aldosteronism diagnosis using Lumipulse® for renin and aldosterone measurements

Objectives Primary aldosteronism (PA) diagnosis is a multistep process that begins with screening based on the plasma aldosterone-to-renin ratio (ARR). As cut-off values depend on the methods used, the aim of this study was 1) to validate the performances of new aldosterone and renin chemiluminescent immuno-assays using the Lumipulse® analyzer (Fujirebio®) and then 2) to define the criteria to distinguish PA patients from those with essential hypertension (EH). Methods This monocentric retrospective study included 297 patients admitted to Georges Pompidou European Hospital, France, between January 2021 and December 2022 for the assessment of hypertension etiology. We evaluated analytical performances and compared Lumipulse® results for plasma renin (PRC) and plasma aldosterone (PAC) concentrations (n = 196) as well as 24-hour urine aldosterone excretion (24UA) (n = 201) to the reference methods (PRC: Liaison XL® Diasorin®; PAC and 24UA: LC-MS/MS). Receiver operating characteristic curve analysis was used to define the cut-off values for ARR, PAC, and 24UA to distinguish between PA and EH patients. Results Analytical characteristics were in accordance with requested performances. Our results showed excellent correlations between Lumipulse® and our reference methods. We propose cut-off values of 35 pmol/mIU as the ARR, 260 pmol/L for PAC, and 49 nmol/24h for 24UA for predicting PA diagnosis. Conclusions Lumipulse® (Fujirebio®) is a reliable alternative for measuring PRC, PAC, and 24UA for predicting PA diagnosis with the use of corresponding cut-off values for each criterion.

First record of Cinara todocola (Hemiptera: Aphididae) on endangered Christmas tree in South Korea: morphology, biology, and global invasion potential

Wastewater surveillance overcomes socio-economic limitations of laboratory-based surveillance when monitoring disease transmission: The South African experience during the COVID-19 pandemic

Wastewater and environmental surveillance has been promoted as a communicable disease surveillance tool because it overcomes inherent biases in laboratory-based communicable disease surveillance. Yet, little empirical evidence exists to support this notion, and it remains largely an intuitive, though highly plausible hypothesis. Our interdisciplinary study uses WES data to show evidence for underreporting of SARS-CoV-2 in the context of measurable and statistically significant associations between economic conditions and SARS-CoV-2 incidence and testing rates. We obtained geolocated, anonymised, laboratory-confirmed SARS-CoV-2 cases, wastewater SARS-CoV-2 viral load data and socio-demographic data for Gauteng Province, South Africa. We spatially located all data to create a single dataset for sewershed catchments served by two large wastewater treatment plants. We conducted epidemiological, persons infected and principal component analysis to explore the relationships between variables. Overall, we demonstrate the co-contributory influences of socio-economic indicators on access to SARS-CoV-2 testing and cumulative incidence, thus reflecting that apparent incidence rates mirror access to testing and socioeconomic considerations rather than true disease epidemiology. These analyses demonstrate how WES provides valuable information to contextualise and interpret laboratory-based epidemiological data. Whilst it is useful to have these associations established for SARS-CoV-2, the implications beyond SARS-CoV-2 are legion for two reasons, namely that biases inherent in clinical surveillance are broadly applicable across pathogens and all pathogens infecting humans will find their way into wastewater albeit in varying quantities. WES should be implemented to strengthen surveillance systems, especially where economic inequalities limit interpretability of conventional surveillance data.

Development of weighted residual RNN model with hybrid heuristic algorithm for movement recognition framework in ambient assisted living

Oseltamivir-induced hepatotoxicity: A retrospective analysis of the FDA adverse event reporting system

Assessing the potential for oseltamivir-induced liver damage is essential to ensure its safe administration. The aim of this study was to examine the association between hepatotoxicity and oseltamivir use and to describe the features of oseltamivir-induced hepatotoxicity. Data were obtained from the Adverse Event Reporting System of the US Food and Drug Administration (FAERS). Disproportionality and proportionality analyses were performed to evaluate the safety profile of oseltamivir-related hepatotoxicity and the occurrence of hepatotoxicity-related adverse events across sex and age groups. The FAERS recorded 20,340,254 adverse event reports between 2004 and 2023, of which 16,960,996 reports were included in the analysis. We identified 14 types of oseltamivir-related adverse events that were hepatotoxic and showed positive signals. The most frequently reported adverse event was abnormal hepatic function (n =  54), and the most severe adverse event was fulminant hepatitis. Compared with that for male individuals, the reporting odds ratio (ROR) was 0.5 for female individuals; and for male individuals, the ROR, compared with that for female individuals, was 4.19. The median time to hepatotoxic adverse events, excluding mixed liver injury, was <  5 days. Oseltamivir can cause liver toxicity, which is influenced by sex and age. Liver function tests and monitoring for signs of liver disease are crucial when using oseltamivir.

Using machine learning to explore the efficacy of administrative variables in prediction of subjective-wellbeing outcomes in New Zealand

Chinese medical named entity recognition utilizing entity association and gate context awareness

Recognizing medical named entities is a crucial aspect of applying deep learning in the medical domain. Automated methods for identifying specific entities from medical literature or other texts can enhance the efficiency and accuracy of information processing, elevate medical service quality, and aid clinical decision-making. Nonetheless, current methods exhibit limitations in contextual awareness and insufficient consideration of contextual relevance and interactions between entities. In this study, we initially encode medical text inputs using the Chinese pre-trained RoBERTa-wwm-ext model to extract comprehensive contextual features and semantic information. Subsequently, we employ recurrent neural networks in conjunction with the multi-head attention mechanism as the primary gating structure for parallel processing and capturing inter-entity dependencies. Finally, we leverage conditional random fields in combination with the cross-entropy loss function to enhance entity recognition accuracy and ensure label sequence consistency. Extensive experiments conducted on datasets including MCSCSet and CMeEE demonstrate that the proposed model attains F1 scores of 91.90% and 64.36% on the respective datasets, outperforming other related models. These findings confirm the efficacy of our method for recognizing named entities in Chinese medical texts.

Urban and rural disparities in stroke prediction using machine learning among Chinese older adults

Effects of dry cupping on exercise, autonomic activity and sleep in baseball players during preseason and in-season conditioning

Background Cupping therapy has been shown to alleviate muscle fatigue, sustain exercise capacity, enhance post-exercise recovery of autonomic activity, and improves sleep quality. However, variations in athletes’ training intensity, competition pressure, and fatigue levels throughout a sports season remain underexplored. Few studies have investigated whether the health benefits of cupping differ across various phases of a sports season. This study aimed to examine the effects of short-term cupping on athletes during preseason conditioning (PSC) and in-season conditioning (ISC). Methods Forty university baseball players were recruited and randomly assigned to either the cupping (dry cupping at − 400 mmHg) or sham (dry cupping at − 100 mmHg) group. Cupping was applied to the upper back and shoulders for 15 minutes, twice a week for 8 consecutive weeks (4 weeks each during PSC and ISC). Results Cupping had no significant effect on upper-extremity function during either PSC or ISC. Exercise tests during PSC and ISC revealed no postintervention changes in peak power, peak oxygen consumption, or anaerobic threshold. However, cupping during PSC improved postexercise recovery of low-frequency power (LF; P = .013; a component of heart rate variability) and that during ISC improved recovery of the LF/high-frequency power ratio (P = .004) and LF% (P = .037). Additionally, cupping during PSC notably enhanced daytime function, as measured by the Pittsburgh sleep quality index (P = .026). Conclusions The benefits of cupping therapy vary between PSC and ISC. Cupping during PSC and ISC notably improved the postexercise recovery of autonomic and sympathetic activities, respectively. However, improvements in sleep quality were only observed during PSC.

A nomogram to predict postoperative overall and cancer specific survival in patients with primary parotid squamous cell carcinoma: a population based study

Research on order batching optimization based on improved NSGA-II algorithm

In the context of e-commerce, the order batching optimization problem in e-commerce warehousing centers has been addressed by establishing a model aimed at minimizing the order picking time, order delay costs, and picking costs, as well as achieving workload balance. An improved NSGA-II algorithm has been designed, which enhances the search capability and solution diversity by introducing new selection mechanisms and crossover mutation strategies. This approach more effectively balances multiple optimization objectives and validates the effectiveness of the model and algorithm with case studies, while also conducting sensitivity analysis on model parameters. The research results indicate that the established model and the designed algorithm are effective, providing a theoretical basis and practical significance for the optimization of order picking efficiency in e-commerce distribution centers.

Effects of gestational weight gain on adverse pregnancy outcomes among pregnant women in gurage zone, central Ethiopia: a cohort study

Exploring language use in apology strategies of tourists visiting Saudi Arabia

This research explores the apology strategies employed by international tourists visiting Saudi Arabia, particularly in the context of the Kingdom’s rapid tourism expansion under Vision 2030. With the introduction of new tourism policies, the country has opened itself to a wide range of international visitors, increasing the likelihood of cultural missteps. The study investigates how tourists navigate Saudi Arabia’s conservative social norms, focusing on factors such as language use, cultural awareness, and the perceived severity of offenses. Data were collected through qualitative focus group interviews with international tourists, analyzing how apology strategies are influenced by social dynamics, gender norms, and power structures. The findings suggest that tourists who demonstrate cultural sensitivity, particularly by using Arabic, are more likely to have their apologies accepted. The study highlights the importance of cross-cultural competence in managing tourist-local interactions and provides insights for tourism operators and policymakers on improving the tourist experience in Saudi Arabia. The research contributes to a deeper understanding of cross-cultural communication in the context of a rapidly growing tourism industry.

Objectively measured moderate-to-vigorous physical activity does not attenuate prospective weight gain among african-origin adults spanning the epidemiological transition

Comparative analysis of acute and chronic painful temporomandibular disorders: Insights into pain, behavioral, and psychosocial features

Objective The scarcity of literature necessitates further research to differentiate between acute and chronic painful temporomandibular disorders (TMDs). This study compared pain characteristics, oral behaviors, jaw function, and psychosocial distress between TMD patients with acute and chronic pain, examined correlations among variables, and identified factors associated with chronic pain-related TMDs (PT). Methods Anonymized data were gathered from consecutive patients seeking TMD treatment at a university-based oral medicine clinic. Axis I diagnoses were made using the Diagnostic Criteria for TMDs, and patients with PT were categorized into acute (AP) and chronic pain (CP) groups. Axis II assessments were performed, evaluating pain characteristics, oral behaviors, jaw functional limitation, somatic symptoms, depression, and anxiety. Statistical analysis utilized chi-square/non-parametric tests and logistic regression (α =  0.05). Results Among the 488 PT patients, 34.6% experienced AP and 65.4% had CP. Significant differences were observed in pain intensity, interference, disability, jaw overuse behavior, functional limitation, somatic symptom burden, depression, and anxiety. (CP>  AP). Moderate-to-strong correlations were found in both the AP (rs =  0.43–0.83) and CP (rs =  0.46–0.87) groups, although the specific relationships between pain, behavioral, and psychosocial factors differed somewhat. The multivariate regression model revealed that only pain intensity (OR =  1.01) and oral behaviors (OR =  1.06) significantly increased the odds of chronic PT. Conclusion Chronic pain was more prevalent in PT patients and associated with greater severity in pain, behavioral, and psychosocial variables. Pain intensity and oral behaviors were linked to an increased likelihood of chronic pain.

Association between oxidative balance score and thyroid function and all-cause mortality in euthyroid adults

Abstract Abnormal fluctuations in thyroid function within the reference range were strongly associated with increased all-cause mortality. This study aimed to analyze the association between oxidative balance score (OBS) and free thyroxine (FT4) and thyrotropin (TSH) in euthyroid adults, as well as their interrelationships with mortality. 5727 euthyroid adults were selected from the National Health and Nutrition Examination Survey (NHANES). Weighted linear regression investigated the potential association of OBS with FT4 and TSH. In addition, COX proportional hazard models and restricted cubic spline (RCS) were used to investigate the association between OBS, FT4, TSH, and all-cause mortality. The results showed that OBS was negatively associated with serum FT4 concentrations in euthyroid adults (− 2.95%, 95% CI − 5.16%, − 0.92%). Additionally, the all-cause mortality rate was significantly higher in the fourth quartile (Q4) of FT4 compared to the first quartile (Q1) (HR 1.40, 95% CI 1.07–1.85). In the fourth quartile of OBS, the all-cause mortality rate was 31% lower than in Q1 (HR 0.69, 95% CI 0.52–0.92). Mediation analyses indicated that FT4 partially mediated the relationship between OBS and all-cause mortality. These results suggest a significant negative association between OBS and serum FT4, while both OBS and FT4 are strongly associated with mortality. However, the effect of OBS on serum FT4 is relatively limited, and therefore its clinical significance needs to be interpreted objectively.

Assessing regional competitiveness in Peru: An approach using nonlinear machine learning models

This study addresses the challenges of measuring regional competitiveness using traditional methods, due to the inherent complexity and non-linearity of its determinants’. The development of new Machine Learning (ML) models allows the creation of predictive models capable of handling this type of data, providing actionable insights. The objective of the study was to develop and test the use of non-linear Machine Learning models to measure the regional competitiveness in Peru, at the sub-national level. The research uses the ODD (Overview, Design Concepts, and Details) protocol to ensure a transparent and replicable methodology. The impact of ML on the Peruvian Regional Competitiveness Index (IRCI) is examined across 25 regions from 2016 to 2023, focusing on five key pillars: economy, government, infrastructure, businesses, and people. A suitability index (IoI) was developed to assess how well the pillar components align with ML. Data provided by CENTRUM PUCP was subjected to exploratory data analysis (EDA) to address variability among pillar scores and their effects on competitiveness. Six nonlinear machine learning models (Gradient Boosting, Random Forest, XGBoost, AdaBoost, Neural Networks, and Decision Trees) were applied, and the machine learning models with the highest predictive accuracy were Gradient Boosting and Random Forest. Performance metrics include MSE values of 1.1399 and 1.3469, RMSE values of 1.0677 and 1.1606, and R2 values of 0.9768 and 0.9729, respectively. These results demonstrate the effectiveness of machine learning in analyzing the complexity of regional competitiveness data, identifying influential variables, and reducing score distortions. The findings provide a data-driven framework for policymakers to improve regional competitiveness, which promotes academic knowledge and practical applications for sustainable development.

Comparison of cryogenic and non-cryogenic droplet impact dynamics at low Weber numbers

Abstract Cryogenic fluids are crucial in applications such as rockets, cryosurgery and energy storage, where they can come in contact with surfaces. Thus, their impact dynamics are of interest. Experiments under cryogenic conditions are very expensive and not always accurate, mainly due to limitations of equipment operating in very low temperatures. Although simulation tools can provide useful insights, currently very few commercial and open-source software tailored for ultra low temperature conditions exist. In this work we present a novel numerical framework used to provide insight into the impact dynamics of cryogenic droplets with solid surfaces. Our aim is to explore whatever conclusions for droplet spreading dynamics from the current literature for droplets at non-cryogenic conditions can be applied to cryogenic droplets as well. We explore different impacting conditions, varying the initial impact velocity, of cryogenic and non-cryogenic cases, while maintaining the same Weber, Ohnesorge and Reynolds numbers between cryogenic and non-cryogenic cases. The impact on a solid surface is investigated first for a water droplet and then for a liquid oxygen droplet moving into gaseous nitrogen. For the latter, the ambient temperature and pressure are below the oxygen critical point, limiting the investigation at a sub-critical regime. The algebraic volume of fluid method with adaptive mesh refinement is employed. Numerical treatments to improve the interface description are also implemented. The simulations have been performed in OpenFOAM with a newly developed code. The results obtained are analysed both qualitatively and quantitatively, comparing the droplet morphology evolution for the two fluids. Differences are observed mainly in the receding stage, once the droplet has reached the maximum spreading, with the receding stage of the cryogenic case characterised by a faster dynamic.

Adaptation and validation of a Rwanda-focused version of the Alcohol Use Disorder Identification Test (AUDIT)

In Rwanda, alcohol use disorder (AUD) is estimated to affect 7% of the population. The Alcohol Use Disorder Identification Test (AUDIT) is an excellent screening instrument for AUD, but a Rwanda-focused version previously was unavailable. Our objective was to develop a Rwanda- focused AUDIT and evaluate its psychometric properties. The English AUDIT was adapted to the Rwandan language through translation and back- translation by a panel of native English and Kinyarwanda speakers. Random sampling was used to recruit participants from the emergency department, outpatient clinics, and inpatient wards at a tertiary care center in Rwanda, excluding those < 18 years old, declining to participate, unable to provide consent, or when participation would interfere with care. Participants completed the Rwanda-focused AUDIT using an audio computer-assisted self-interviewing format. Internal structure was assessed using one-, two- and three-dimensional models of fit and confirmatory factor analysis (CFA), assessed by Chi-square (χ2), Root Mean Square Error of Approximation (RMSEA), Tucker-Lewis index (TLI) and comparative fit index (CFI). Of 775 patients assessed for enrollment, 7% were unable to provide consent, 12% declined to participate, 2% could not participate because it would disrupt their medical care, and 1.3% dropped out, leaving 614 included for analysis. Of the 614, the majority were male (61%), married (53%) and had only primary education (65%). Their ages were: 33% 18-30, 43% 31-50, and 25% > 50 years-old. Factor loading for the AUDIT CFA model was between 0.62 and 0.96 for all items. Model fit indices included χ2 of < 0.001, RMSA of 0.061 (0.049 - 0.073), TLI of 0.994, and CFI of 0.995. Reliability statistics included Cronbach’s alpha at 0.91 (0.90 – 0.92), Omega 6 at 0.948 and composite reliability at 0.977. The Rwanda-focused AUDIT showed excellent performance for measures of internal structure with high factor loading on CFA and model fit indices meeting traditional parameters of RMSEA < 0.08, TLI > 0.90, and CFI > 0.95. In this context, χ2 should ideally be > 0.05, however a relatively large sample, such as ours, tends to depress the number. All reliability statistics were above 0.90, indicating strong internal consistency. These findings support the reliability of this screening instrument. Further research should focus on the development of brief interventions for those who screen positive.