BACKGROUND: The causes of symptoms in patients with paroxysmal atrial fibrillation (AF) remains unclear.
OBJECTIVE: The purpose of this study was to correlate the magnitudes of skin sympathetic nerve activity (SKNA) with symptoms in patients with AF.
METHODS: We prospectively enrolled patients with symptomatic paroxysmal AF for ambulatory electrocardiography and SKNA recording. Heart rhythms at the time of symptoms were categorized as AF or normal sinus rhythm (NSR). Maximal and average skin sympathetic nerve activity (aSKNA) and heart rate (HR) were compared between symptomatic and asymptomatic AF and NSR episodes using mixed effects models to account for within-patient correlations.
RESULTS: Among the 31 enrolled patients, 16 (52%) had at least 1 episode of AF, and 24 (77%) endorsed symptoms during the monitoring period. Compared with asymptomatic AF episodes, symptomatic AF episodes had higher maximal aSKNA (1.260 [interquartile range (IQR) 1.114-1.723] μV vs 1.108 [IQR 0.974-1.312] μV; P <0.001) and higher maximal HR (152 ± 24 bpm vs 132 ± 19 bpm; P <.001). Symptomatic NSR episodes were associated with higher maximal aSKNA (1.612 [IQR 1.287-2.027] μV vs 1.332 [IQR 1.033-1.668] μV; P = .001) and higher maximal HR (152 ± 24 bpm vs 105 ± 16 bpm; P <.001) than asymptomatic NSR episodes. Of the symptomatic episodes, 66 (73%) occurred during NSR and 24 (27%) during AF. All P values were obtained from mixed effects models.
CONCLUSIONS: Symptomatic episodes in patients with paroxysmal AF were more frequently associated with NSR than AF. Symptomatic AF and NSR episodes were associated with higher aSKNA than asymptomatic episodes. In patients with paroxysmal AF, symptoms correlate better with SKNA than heart rhythm.

Despite a decrease in the prevalence of low birth weight (LBW) over time, its ongoing significance as a public health concern in Bangladesh remains evident. Low birth weight is believed to be a contributing factor to infant mortality, prolonged health complications, and vulnerability to non-communicable diseases. This study utilizes nationally representative data from the Multiple Indicator Cluster Surveys (MICS) conducted in 2012-2013 and 2019 to explore factors associated with birth weight. Modeling birth weight data considers interactions among factors, clustering in data, and spatial correlation. District-level maps are generated to identify high-risk areas for LBW. The average birth weight has shown a modest increase, rising from 2.93 kg in 2012-2013 to 2.96 kg in 2019. The study employs a regression tree, a popular machine learning algorithm, to discern essential interactions among potential determinants of birth weight. Findings from various models, including fixed effect, mixed effect, and spatial dependence models, highlight the significance of factors such as maternal age, household head\'s education, antenatal care, and few data-driven interactions influencing birth weight. District-specific maps reveal lower average birth weights in the southwestern region and selected northern districts, persisting across the two survey periods. Accounting for hierarchical structure and spatial autocorrelation improves model performance, particularly when fitting the most recent round of survey data. The study aims to inform policy formulation and targeted interventions at the district level by utilizing a machine learning technique and regression models to identify vulnerable groups of children requiring heightened attention.

BACKGROUND: We aimed to evaluate the impacts of short-term daily temperature variability (DTV) on blood pressure (BP) among participants with normotension, prehypertension, and hypertension, respectively, and explore the effects in different climate zones and seasons.
METHODS: A representative population sample (n = 397,173) covering the subtropical, temperate continental, and temperate monsoon zones was obtained from the China Hypertension Survey. DTV was calculated as the standard deviation of daily minimum and maximum temperatures during the exposure days. The linear mixed effect regression model was used to estimate the associations between DTV exposure and BP among normotension, prehypertension, and hypertension, respectively, and further stratified analysis was performed by climate zones and seasons.
RESULTS: After adjustment for confounders, per interquartile range (IQR) increase in DTV (2.28 °C) at 0-6 days of exposure was associated with an increase of 0.41 mmHg (95 % confidence interval [CI]: 0.07, 0.75) in systolic BP (SBP) and 0.41 mmHg (95 % CI: 0.09, 0.72) in pulse pressure (PP) among hypertensive participants in the subtropical zone. Similarly, DTV exposure was associated with an increase of 0.31 mmHg (95 % CI: 0.06, 0.55) in SBP and 0.59 mmHg (95 % CI: 0.24, 0.94) in PP among prehypertensive participants in the temperate continental zone. Additionally, during the warm season, DTV was positively associated with SBP among populations with prehypertension and hypertension, and with PP among all three populations.
CONCLUSIONS: Short-term DTV exposure was associated with an increase in SBP and PP among hypertensive and prehypertensive participants in the subtropical zone and the temperate continental zone. In addition, positive associations of DTV with SBP and PP were observed among participants with prehypertension and hypertension in the warm season. Comprehensive health education and effective intervention strategies should be implemented to mitigate the effects of temperature variations on BP, particularly among prehypertensive and hypertensive populations.

The experiment is designed to evaluate the yield performance and profitability of mung bean varieties and to survey the innovation inclination of the farmers in the study area. The experiment was carried out in the potential environments of the North Shewa zone for two years during the main growing period. As experimental treatments, four mung bean cultivars of Rasa, NLV-1, Arkebe, and local varieties were used and evaluated across the four environments (two farmers\' fields per each environment). The experimental plots were arranged in a simple random block design. The result of the combined analysis of variance revealed that there was highly significant variation (p < 0.01) of grain yield among the genotypes while the environments and genotype by environment interaction are found insignificant. The highest mean grain yield of 1430.6 kg ha-1 was obtained from the improved variety Rasa, which was selected first by the farmers followed by the variety NVL-1. The results also confirmed the existence of a strong and statistically significant association between the actual values rank and the farmers\' preference rank for both grain and biomass yields (R = .80, p < .001). Also, the variety Rasa provides the highest (686.6%) marginal rate of return on investment. Therefore, by considering the results of the grain yield performance, farmers\' selection, and the result of the partial budget analysis the variety Rasa was recommended for the study areas.

PM2.5 pollution is a serious problem in Vietnam and around the world, having bad impacts on human health, animals and environment. Regular monitoring at a large scale is important to assess the status of air pollution, develop solutions and evaluate the effectiveness of policy implementation. However, air quality monitoring stations in Vietnam are limited. In this article, we propose an approach to estimate daily PM2.5 concentration from 2012 to 2020 over the Vietnamese territory, which is strongly affected by cloudy conditions, using a modern statistical model named Mixed Effect Model (MEM) on a dataset consisting of ground PM2.5 measurements, integrated satellite Aerosol Optical Depth (AOD), meteorological and land use maps. The result of this approach is the first long-term, full coverage and high quality PM2.5 dataset of Vietnam. The daily mean PM2.5 maps have high validation results in comparison with ground PM2.5 measurement (Pearson r of 0.87, R2 of 0.75, RMSE of 11.76 μg/m3, and MRE of 36.57 % on a total of 13,886 data samples). The aggregated monthly and annual average maps from 2012 to 2020 in Vietnam have outstanding quality when compared with another global PM2.5 product. The PM2.5 concentration maps has shown spatial distribution and seasonal variations of PM2.5 concentration in Vietnam in a long period from 2012 to 2020 and has been used in other studies and applications in the environment and public health at the national scale, which has not been possible before because of the lack of monitoring stations and an appropriate PM2.5 modeling approach.

Despite a substantial number of COVID-19 related research papers published, it remains unclear as to which factors are associated with the observed variation in global transmission and what are their relative levels of importance. This study applies a rigorous statistical framework to provide robust estimations of the factor effects for a global and integrated perspective on this issue. We developed a mixed effect model exploring the relative importance of potential factors driving COVID-19 transmission while incorporating spatial and temporal heterogeneity of spread. We use an integrated data set for 87 countries across six continents for model specification and fitting. The best model accounts for 70.4% of the variance in the data analyzed: 10 fixed effect factors explain 20.5% of the variance, random temporal and spatial effects account for 50% of the variance. The fixed effect factors are classified into climatic, demographic and disease control groups. The explained variance in global transmission by the three groups are 0.6%, 1.1%, and 4.4% respectively. The high proportion of variance accounted for by random effects indicated striking differences in temporal transmission trajectories and effects of population mobility among the countries. In particular, the country-specific mobility-transmission relationship turns out to be the most important factor in explaining the observed global variation of transmission in the early phase of COVID-19 pandemic.

A mathematical mixed effect model was established to analyse the factors of neonatal hypoglycaemia of gestational diabetes mellitus (GDM). 229 cases of GDM patients were enrolled in this study. The data were analysed by logarithmic transformation of non-normal distribution. Furthermore, the mathematical model was used to analyse influencing factors of hypoglycaemia of neonatal from women with GDM. The results showed that the blood glucose distribution level had a trend of increasing with time, which indicates that it is necessary to strengthen blood glucose intervention of newborns from GDM maternal and provides a data for the timely detection of hypoglycaemia in GDM newborns. Furthermore, we successfully established the GDM newborn blood glucose level mixed mathematical model. From this model, we calculated the GDM newborn blood glucose normal confidence interval based on mixed factors. The results indicate that the minimum value of blood glucose level in GDM newborns did not exceed the risk level 2.2 mmol/L. We concluded that the mathematical mixed effect model is successfully established, from which the change discipline of blood glucose level of newborn from GDM parturient are found. Impact statementWhat is already known on this subject? The morbidity of gestational diabetes mellitus (GDM) in China has been increased. However, the clinical data is difficult to be collected and the data that is used for statistics is not enough, which makes it difficult to understand the neonatal hypoglycaemia of GDM more clearly.What do the results of this study add? In this study, we successfully established a mathematical mixed effect model of neonatal hypoglycaemia of women with GDM, which can investigate the influence factor of hypoglycaemia of newborn from women with GDM to find the discipline of blood glucose level of newborn from GDM parturient via mathematical model.What are the implications of these findings for clinical practice and/or further research? Our research helps to better understand and improve the health problem of pregnant women with GDM and their newborn babies.

Due to the evolving variants of coronavirus disease 2019 (COVID-19), it is important to understand the relationship between the disease condition and socioeconomic, demographic, and health indicators across regions.
Studies examining the relationships between infectious disease and socioeconomic variables are not yet well established.
A total of 3042 counties in the United States are included as the observation unit in the study. Two outcome variables employed in the study are the control of disease spread and infection prevalence rates in each county.
Data are submitted to quantile regression, hierarchical regression, and random forest analyses to understand the extent to which health outcomes are affected by demographics, socioeconomics, and health indicators.
Counties with better control of the disease spread tend to have lower infection rates, and vice versa. When measuring different outcome variables, the common risk factors for COVID-19 with a 5% level of statistical significance include employment ratio, female labor ratio, young population ratio, and residents\' average health risk factors, while protective factors include land size, housing value, travel time to work, female population ratio, and ratio of residents who identify themselves as mixed race.
The implications of the findings are that the ability to maintain social distancing and personal hygiene habits are crucial in deterring disease transmission and lowering incidence rates, especially in the early stage of disease formation. Relevant authorities should identify preventive factors and take early actions to fight infectious diseases in the future.

(1) Background: The aim of this study was to assess the preventive effect of tooth surface disinfection treatment, in addition to fluoride application, during fixed orthodontic treatment. (2) Methods: An open label randomized control trial for the evaluation of the dental caries preventive procedure was performed for the patients with high caries risk who had been visited at Department of Orthodontics, Tsurumi University Dental Hospital for orthodontics treatment. The follow-up period was six months. White spot lesions (WSLs) were evaluated by quantitative light-induced fluorescence (QLF). Cariogenic bacteria were monitored and evaluated by bacterial culture. In addition, the oral microbiome was evaluated by a next-generation sequence (NGS). (3) Results: By the mixed effect modeling, tooth surface disinfection treatment significantly reduced cariogenic bacteria and all parameters obtained by QLF. (4) Conclusions: Tooth surface disinfection treatment, in addition to PMTC and fluoride application, were effective for dental caries prevention and keeping a healthy microbiome during orthodontic treatment.

High concentrations of respirable quartz have been reported from workers in construction, foundries, and quarries. Current exposure concentrations in prevalent but presumably lower exposed occupations have been less examined. We aimed to quantify current exposure concentrations of respirable dust and quartz across prevalent occupations and to identify determinants of respirable quartz exposure across these occupations.
One hundred and eighty-nine full-shift personal samples of respirable dust of workers within 11 occupations in Denmark were sampled during 2018. Respirable dust was determined gravimetrically and analysed for quartz content with infrared spectrometry. Determinants for respirable quartz exposure, i.e. use of power tools, outdoor or indoor location, and percentage of quartz in respirable dust, were analysed in linear mixed effect models.
The overall geometric means (geometric standard deviations) for respirable dust and quartz were 216 µg m-3 (4.42) and 16 µg m-3 (4.07), respectively. The highest quartz concentrations were observed among stone cutters and carvers [93 µg m-3 (3.47)], and metal melters and casters [61 µg m-3 (1.71)]. Use of power tools increased exposure concentrations of quartz by a factor of 3.5. Occupations explained 27%, companies within occupations 28%, and differences between workers within companies within occupations 14% of the variability in quartz concentrations. Thirty percent was due to day-to-day variability in exposure concentrations. In total, 19% of the variation in quartz concentration could be explained by type of tool, indoor/outdoor location, and percentage of quartz in respirable dust.
Current exposure concentrations are generally low, but some occupations in this study had average exposure concentrations to respirable quartz above the ACGIH threshold limit value of 25 µg m-3. Preventive measures to lower excess risk of quartz-related diseases among these workers are still needed. In terms of preventive strategies, use of power tools and quartz content of used materials were identified as main determinants of exposure. Lowering of exposures will be most efficient when focussed on these major determinants, e.g. tool dust control with water, dust extraction, and use of low quartz content materials.