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BACKGROUND: Healthy siblings of children with life-limiting conditions often experience emotional and behavioural struggles over the course of the ill child's condition(s). Resources to support these siblings are limited due to a lack of understanding about their needs. Therefore, this study was designed to characterize the emotional and behavioural trajectories among siblings of children with progressive, life-limiting genetic, metabolic, or neurological conditions over a 12-month observation period. METHODS: Seventy siblings were recruited from a large-survey based study (Charting the Territory) that examined the bio-psychosocial health outcomes of parents and siblings. Linear mixed effect models were used to assess the association between siblings' emotions and behaviour trajectories and selected demographic variables. Siblings' emotions and behaviour were measured with Child Behaviour Checklist (CBCL). RESULTS: Siblings' mean age was 11.2 years at baseline and Internalizing, Externalizing, and Total Behaviour Problems mean scores were within normal ranges across time. However, 7-25% of siblings had scores within the clinical range. Brothers had higher levels of Internalizing Problems than sisters, whereas sisters had higher levels of Externalizing Problems than brothers. When treatment was first sought for the ill child less than a year prior to study participation, siblings had higher levels of Internalizing and Externalizing Problems compared with siblings who participated more than one year after treatment was sought. CONCLUSION: Healthy siblings experience emotional and behavioural problems early in the child's disease trajectory. Although these problems improve with time, our findings show that brothers and sisters experience different types of challenges. Therefore, timely support for siblings is important as they navigate through the uncertainties and challenges.

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To unravel how within-person psychological processes fluctuate in daily life, and how these processes differ between persons, intensive longitudinal (IL) designs in which participants are repeatedly measured, have become popular. Commonly used statistical models for those designs are multilevel models with autocorrelated errors. Substantive hypotheses of interest are then typically investigated via statistical hypotheses tests for model parameters of interest. An important question in the design of such IL studies concerns the determination of the number of participants and the number of measurements per person needed to achieve sufficient statistical power for those statistical tests. Recent advances in computational methods and software have enabled the computation of statistical power using Monte Carlo simulations. However, this approach is computationally intensive and therefore quite restrictive. To ease power computations, we derive simple-to-use analytical formulas for multilevel models with AR(1) within-person errors. Analytic expressions for a model family are obtained via asymptotic approximations of all sample statistics in the precision matrix of the fixed effects. To validate this analytical approach to power computation, we compare it to the simulation-based approach via a series of Monte Carlo simulations. We find comparable performances making the analytic approach a useful tool for researchers that can drastically save them time and resources.

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OBJECTIVES: Given the plausible mechanisms and the lacking of empirical evidence, the study aims to investigate how gestational sleep behaviors and the development of sleep disorders, such as restless legs syndrome, influence ultrasonographic measures of fetal growth. METHODS: The study included 2457 pregnant women from the NICHD Fetal Growth Studies - Singletons (2009-2013), who were recruited between 8-13 gestational weeks and followed up to five times during pregnancy. Women were categorized into six groups based on their total sleep hours and napping frequency. The trajectory of estimated fetal weight from 10-40weeks was derived from three ultrasonographic measures. Linear mixed effect models were applied to model the estimated fetal weight in relation to self-reported sleep-napping behaviors and restless legs syndrome status, adjusting for age, race and ethnicity, education, parity, prepregnancy body mass index category, infant sex, and prepregnancy sleep-napping behavior. RESULTS: From enrollment to near delivery, pregnant women's total sleep duration and nap frequency declined and restless legs syndrome symptoms frequency increased generally. No significant differences in estimated fetal weight were observed by sleep-napping group or by restless legs syndrome status. Results remained similar in sensitivity analyses and stratified analyses by women's prepregnancy body mass index category (normal vs. overweight/obese) or by infant sex. CONCLUSIONS: Our data indicate that there is no association between sleep during pregnancy-assessed as total sleep duration and napping frequency, nor restless legs syndrome symptoms-and fetal growth from weeks 10 to 40 in healthy pregnant women.

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Greenspaces are crucial for enhancing mental and physical health. Recent research has shifted from static methods of assessing exposure to greenspaces, based on fixed locations, to dynamic approaches that account for individual mobility. These dynamic evaluations utilize advanced technologies like GPS tracking and remote sensing to provide more precise exposure estimates. However, little work has been conducted to compare dynamic and static exposure assessments and the effect of individual mobility on these evaluations. This study delves into how greenspaces around homes and workplaces, along with mobility patterns, affect dynamic greenspace exposure in Hong Kong. Data was collected from 787 participants in four communities in Hong Kong using GPS, portable sensors, and surveys. Using multiple statistical tests, our study revealed significant variations in participants' daily mobility patterns across socio-demographic and temporal factors. Further, using linear mixed-effects models, we identified complex and statistically significant interactions between participants' static greenspace exposure and their mobility patterns. Our findings suggest that individual mobility patterns significantly modify the relationship between static and dynamic greenspace exposure and play a critical role in explaining socio-demographic and temporal context differences in the relationship between static and dynamic greenspace exposure.

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Animals face several challenges in their natural environment, and to cope with such conditions, they may exhibit contrasting physiological responses that directly affect their overall well-being and survival. In this study, we assessed physiological responses via faecal glucocorticoid metabolite (fGCM) measurements in free-ranging mugger crocodiles inhabiting diverse habitats in Gujarat, India. We sampled muggers within Charotar, a rural area (Zone A) with local people having high tolerance towards the presence of muggers, and Vadodara, a region having both urban (Zone B) and rural (Zone C) areas with high levels of human-mugger conflict (HMC). Further, muggers in Vadodara live in water bodies that are mostly polluted due to sewage disposal from adjoining chemical industries. To measure fGCM (mean ± SEM, ng/g dry faeces) levels in muggers, scats were collected during both breeding (N = 107 scats) and non-breeding (N = 22 scats) seasons from all three zones. We used captive muggers (a focal enclosure) to biologically validate (via capture and restraint) the selected fGCM assay (11-oxoetiocholanolone assay). We showed a significant (P < 0.05) 11-fold increase in fGCM levels between pre-capture (540.9 ± 149.2, N = 11) and post-capture (6259.7 ± 1150.5, N = 11) samples. The validated assay was applied to free-ranging muggers during the breeding season, and Zone A showed significantly (P < 0.05) lower fGCM levels (542.03 ± 71.3) compared to muggers of Zone B (1699.9 ± 180.8) and Zone C (1806.4 ± 243.2), both zones having high levels of HMC with polluted water bodies. A similar contrast in fGCM levels was also observed during the non-breeding season. Overall, the study demonstrated that fGCM levels in muggers varied across habitats, and such variation could be due to a multitude of ecological factors that the species experience in their immediate local environment. Moreover, high fGCM levels in muggers of Vadodara during both breeding and non-breeding seasons may indicate a condition of chronic stress, which could be maladaptive for the species.

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Conventional pharmacokinetic (PK) bioequivalence (BE) studies aim to compare the rate and extent of drug absorption from a test (T) and reference (R) product using non-compartmental analysis (NCA) and the two one-sided test (TOST). Recently published regulatory guidance recommends alternative model-based (MB) approaches for BE assessment when NCA is challenging, as for long-acting injectables and products which require sparse PK sampling. However, our previous research on MB-TOST approaches showed that model misspecification can lead to inflated type I error. The objective of this research was to compare the performance of model selection (MS) on R product arm data and model averaging (MA) from a pool of candidate structural PK models in MBBE studies with sparse sampling. Our simulation study was inspired by a real case BE study using a two-way crossover design. PK data were simulated using three structural models under the null hypothesis and one model under the alternative hypothesis. MB-TOST was applied either using each of the five candidate models or following MS and MA with or without the simulated model in the pool. Assuming T and R have the same PK model, our simulation shows that following MS and MA, MB-TOST controls type I error rates at or below 0.05 and attains similar or even higher power than when using the simulated model. Thus, we propose to use MS prior to MB-TOST for BE studies with sparse PK sampling and to consider MA when candidate models have similar Akaike information criterion.

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Disorders related to gut health are a significant cause of morbidity among athletes in wheelchair. This pilot feasibility trial aims to investigate whether probiotics compared to prebiotics can improve inflammatory status and gut microbiome composition in elite athletes in wheelchair. We conducted a 12-week, randomized, cross-over controlled trial involving 14 elite Swiss athletes in wheelchair. Participants were given a multispecies-multistrain probiotic or prebiotic (oat bran) daily for 4 weeks (Clinical trials.gov NCT04659408 09/12/2020). This was followed by a 4-week washout and then crossed over. Thirty inflammatory markers were assessed using bead-based multiplex immunoassays (LegendPlex) from serum samples. The gut microbiome was characterized via 16S rRNA sequencing of stool DNA samples. Statistical analyses were conducted using linear mixed-effect models (LMM). At baseline, most athletes (10/14) exhibited low levels of inflammation which associated with higher gut microbiome alpha diversity indices compared to those with high inflammation levels. The use of probiotic had higher decrease in 25 (83%) inflammatory markers measured compared to prebiotic use. Probiotic has the potential in lowering inflammation status and improving the gut microbiome diversity. The future trial should focus on having sufficient sample sizes, population with higher inflammation status, longer intervention exposure and use of differential abundance analysis.

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Assessing the bacterial community composition across cacao crops is important to understand its potential role as a modulator of cadmium (Cd) translocation to plant tissues under field conditions; Cd mobility between soil and plants is a complex and multifactorial problem that cannot be captured only by experimentation. Although microbes have been shown to metabolize and drive the speciation of Cd under controlled conditions, regardless of the link between soil bacterial community (SBC) dynamics and Cd mobilization in the rhizosphere, only a few studies have addressed the relationship between soil bacterial community composition (SBCC) and Cd content in cacao seeds (Cdseed). Therefore, this study aimed to explore the association between SBCC and different factors influencing the distribution of Cd across cacao crop systems. This study comprised 225 samples collected across five farms, where we used an amplicon sequencing approach to characterize the bacterial community composition. The soil Cd concentration alone (Cdsoil) was a poor predictor of Cdseed. Still, we found that this relationship was more apparent when the variation within farms was controlled, suggesting a role of heterogeneity within farms in modulating Cd translocation and, thus, seed Cd content. Our results provide evidence of the link between soil bacterial communities and the distribution of Cd across Colombian cacao crops, and highlight the importance of incorporating fine-spatial-scale studies to advance the understanding of factors driving Cd uptake and accumulation in cacao plants. IMPORTANCE: Cadmium (Cd) content in cacao crops is an issue that generates interest due to the commercialization of chocolate for human consumption. Several studies provided evidence about the non-biological factors involved in its translocation into the cacao plant. However, factors related to this process, including soil bacterial community composition (SBCC), still need to be addressed. It is well known that soil microbiome could impact compounds' chemical transformation, including Cd, on the field. Here, we found the first evidence of the link between soil bacterial community composition and Cd concentration in cacao soils and seeds. It highlights the importance of including the variation of bacterial communities to assess the factors driving the Cd translocation into cacao seeds. Moreover, the results highlight the relevance of the spatial heterogeneity within and across cacao farms, influencing the variability of Cd concentrations.

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BACKGROUND: The objective of the present study is to evaluate the association between longitudinal and survival outcomes in the presence of competing risk events. To illustrate the application of joint modeling in clinical research, we assessed the blood oxygen saturation (SPO2) and its association with survival outcomes in coronavirus disease (COVID-19). MATERIALS AND METHODS: In this prospective cohort study, we followed 300 COVID-19 patients, who were diagnosed with severe COVID-19 in the Rohani Hospital in Babol, the north of Iran from October 22, 2020 to March 5, 2021, where death was the event of interest, surviving was the competing risk event and SPO2 was the longitudinal outcome. Joint modeling analyses were compared to separate analyses for these data. RESULT: The estimation of the association parameter in the joint modeling verified the association between longitudinal outcome SPO2 with survival outcome of death (Hazard Ratio (HR) = 0.33, P = 0.001) and the competing risk outcome of surviving (HR = 4.18, P < 0.001). Based on the joint modeling, longitudinal outcome (SPO2) decreased in hypertension patients (ß = -0.28, P = 0.581) and increased in those with a high level of SPO2 on admission (ß = 0.75, P = 0.03). Also, in the survival submodel in the joint model, the risk of death survival outcome increased in patients with diabetes comorbidity (HR = 4.38, P = 0.026). CONCLUSION: The association between longitudinal measurements of SPO2 and survival outcomes of COVID-19 confirms that SPO2 is an important indicator in this disease. Thus, the application of this joint model can provide useful clinical evidence in the different areas of medical sciences.

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The aim of this study was to develop a model to predict individual subject disease trajectories including parameter uncertainty and accounting for missing data in rare neurological diseases, showcased by the ultra-rare disease Autosomal-Recessive Spastic Ataxia Charlevoix Saguenay (ARSACS). We modelled the change in SARA (Scale for Assessment and Rating of Ataxia) score versus Time Since Onset of symptoms using non-linear mixed effect models for a population of 173 patients with ARSACS included in the prospective real-world multicenter Autosomal Recessive Cerebellar Ataxia (ARCA) registry. We used the Multivariate Imputation Chained Equation (MICE) algorithm to impute missing covariates, and a covariate selection procedure with a pooled p-value to account for the multiply imputed data sets. We then investigated the impact of covariates and population parameter uncertainty on the prediction of the individual trajectories up to 5 years after their last visit. A four-parameter logistic function was selected. Men were estimated to have a 25% lower SARA score at disease onset and a moderately higher maximum SARA score, and time to progression (T50) was estimated to be 35% lower in patients with age of onset over 15 years. The population disease progression rate started slowly at 0.1 points per year peaking to a maximum of 0.8 points per year (at 36.8 years since onset of symptoms). The prediction intervals for SARA scores 5 years after the last visit were large (median 7.4 points, Q1-Q3: 6.4-8.5); their size was mostly driven by individual parameter uncertainty and individual disease progression rate at that time.

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BACKGROUND: The unequal distribution of government health spending within African regional economic groupings is a significant barrier to achieving Universal Health Coverage and reaching health-related Sustainable Development targets. It also hampers the progress toward achieving the African Union's vision of an integrated and prosperous Africa, free of its heavy disease burden. Based on panel data from 36 countries nested into eight Regional Economic Communities (RECs), this study probes the effects of countries' macro-level factors on government health expenditure disparities within eight regional economic communities from 2000 to 2019. METHOD: We use the multilevel linear mixed-effect method to show whether countries' trade gains, life expectancy at birth, poverty, urbanization, information and communication technology, and population aging worsen or reduce the differences for two government health expenditure indicators. RESULTS: The insignificant effect of GDP per capita suggests that in most regional economic groupings, the health sector is still not considered a high-priority sector regarding overall government expenditures. Countries' poverty levels and urbanization increase the domestic general government health expenditure disparities as a percentage of general government expenditure within the regional groupings. However, trade gains and ICT diffusion reduce these disparities. Furthermore, the results reveal that external health expenditure per capita and life expectancy at birth positively impact within-regional inequalities in the domestic general government health expenditure per capita. In contrast, GDP per capita and trade gains tend to reduce them. CONCLUSIONS: This study enriches the research on the determinants of government health expenditure inequality in Africa. Policies that can spur growth in trade and ICT access should be encouraged. Countries should also make more efforts to reduce poverty. Governments should also develop policies promoting economic growth and planned urbanization.

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Brain age is an estimate of chronological age obtained from T1-weighted magnetic resonance images (T1w MRI), representing a straightforward diagnostic biomarker of brain aging and associated diseases. While the current best accuracy of brain age predictions on T1w MRIs of healthy subjects ranges from two to three years, comparing results across studies is challenging due to differences in the datasets, T1w preprocessing pipelines, and evaluation protocols used. This paper investigates the impact of T1w image preprocessing on the performance of four deep learning brain age models from recent literature. Four preprocessing pipelines, which differed in terms of registration transform, grayscale correction, and software implementation, were evaluated. The results showed that the choice of software or preprocessing steps could significantly affect the prediction error, with a maximum increase of 0.75 years in mean absolute error (MAE) for the same model and dataset. While grayscale correction had no significant impact on MAE, using affine rather than rigid registration to brain atlas statistically significantly improved MAE. Models trained on 3D images with isotropic 1mm3 resolution exhibited less sensitivity to the T1w preprocessing variations compared to 2D models or those trained on downsampled 3D images. Our findings indicate that extensive T1w preprocessing improves MAE, especially when predicting on a new dataset. This runs counter to prevailing research literature, which suggests that models trained on minimally preprocessed T1w scans are better suited for age predictions on MRIs from unseen scanners. We demonstrate that, irrespective of the model or T1w preprocessing used during training, applying some form of offset correction is essential to enable the model's performance to generalize effectively on datasets from unseen sites, regardless of whether they have undergone the same or different T1w preprocessing as the training set.

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Springs offer insights into groundwater dynamics. Long-term monitoring of spring yields can reflect the response of groundwater storage to climate change. We analyzed the yield trends of 136 springs across 18 hydrogeological regions in Czechia from 1971 to 2020. The trend-free pre-whitening Mann-Kendall test and linear mixed-effects models were used to assess environmental impacts on spring yields. Overall, 71 % of the springs showed no long-term trends, 28 % exhibited decreasing trends, and 1.5 % showed increasing trends in annual spring yields. Altitude has been demonstrated as a contributing factor influencing spring responses to climate change. Lowland springs (<300 m a.s.l.) exhibited the highest proportion of decreasing annual trends (41 %), while uplands (300-600 m a.s.l.) and highlands (>600 m a.s.l.) showed declines in 26 % and 25 % of springs, respectively. Moreover, highlands recorded a 7 % yield increase, indicating a complex interplay between altitude and spring response to climatic factors. A strong positive correlation was found between precipitation and yields (p < 0.01), whereas temperature increases negatively affected spring yields (p < 0.01). The interaction between temperature changes and region transmissivity highlighted the vulnerability of springs in low-transmissivity regions, predominantly those in crystalline and flysch bedrock areas, to climatic shifts. Generally, these regions have lower spring yields compared to the high-transmissivity areas of the Cretaceous basins. Although these lower-yield regions are not used as a primary water source for large areas, unlike regions with high-transmissivity bedrock, they provide water resources for local supply. Analysis of annual spring maxima frequencies revealed a shift in the culmination of maxima occurrences from April to March, with a significant decrease in April (p < 0.05) and May (p < 0.1) and an increase in March (p < 0.05), suggesting a change in spring yield seasonality. The 2015-2020 drought significantly accelerated declining spring yield trends across hydrogeological regions.

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Evaluating drug use within populations in the United States poses significant challenges due to various social, ethical, and legal constraints, often impeding the collection of accurate and timely data. Here, we aimed to overcome these barriers by conducting a comprehensive analysis of drug consumption trends and measuring their association with socioeconomic and demographic factors. From May 2022 to April 2023, we analyzed 208 wastewater samples from eight sampling locations across six wastewater treatment plants in Southern Nevada, covering a population of 2.4 million residents with 50 million annual tourists. Using bi-weekly influent wastewater samples, we employed mass spectrometry to detect 39 analytes, including pharmaceuticals and personal care products (PPCPs) and high risk substances (HRS). Our results revealed a significant increase over time in the level of stimulants such as cocaine (pFDR=1.40×10-10) and opioids, particularly norfentanyl (pFDR =1.66×10-12), while PPCPs exhibited seasonal variation such as peak usage of DEET, an active ingredient in insect repellents, during the summer (pFDR =0.05). Wastewater from socioeconomically disadvantaged or rural areas, as determined by Area Deprivation Index (ADI) and Rural-Urban Commuting Area Codes (RUCA) scores, demonstrated distinct overall usage patterns, such as higher usage/concentration of HRS, including cocaine (p=0.05) and norfentanyl (p=1.64×10-5). Our approach offers a near real-time, comprehensive tool to assess drug consumption and personal care product usage at a community level, linking wastewater patterns to socioeconomic and demographic factors. This approach has the potential to significantly enhance public health monitoring strategies in the United States.

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A great deal of literature has been established for regression analysis of longitudinal data and in particular, many methods have been proposed for the situation where there exist some change points. However, most of these methods only apply to continuous response and focus on the situations where the change point only occurs on the response or the trend of the individual trajectory. In this article, we propose a new joint modeling approach that allows not only the change point to vary for different subjects or be subject-specific but also the effect heterogeneity of the covariates before and after the change point. The method combines a generalized linear mixed effect model with a random change point for the longitudinal response and a log-linear regression model for the random change point. For inference, a maximum likelihood estimation procedure is developed and the asymptotic properties of the resulting estimators, which differ from the standard asymptotic results, are established. A simulation study is conducted and suggests that the proposed method works well for practical situations. An application to a set of real data on COVID-19 is provided.

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Parasites are known for their ability to rapidly adapt to changing conditions. For parasitic helminths, changes in climate, along with farming and management practices associated with the intensification of livestock farming, provide novel challenges which can impact on their epidemiology and control. The sustainability of livestock production partially relies on effective control of helminth infection. Therefore, understanding changes in parasite behaviour, and what drives these, is of great importance. Nematodirus battus is an economically important helminth in the UK and temperate regions. Its infective larvae typically overwinter in eggs on pasture and hatch synchronously in spring, causing acute disease in lambs. Attempts to control disease typically rely on whole-flock benzimidazole (BZ) treatments. In recent years, the emergence of BZ-resistance, alongside the hatching of eggs without the classical over-winter 'chill stimulus', have made N. battus more difficult to control. In three previous studies, after collecting a large number of N. battus populations alongside farm management data from commercial farms, we explored the prevalence of genetic mutations associated with BZ-resistance (n = 253 farms), the ability of eggs to hatch with and without a chill stimulus (n = 90 farms) and how farm management practices varied throughout the UK (n = 187 farms). In the present study, we identify factors which may be acting as drivers, or barriers, to either the development of resistance or the variable hatching behaviour of N. battus eggs. Generalised linear mixed effect models were applied to regress experimental hatching and genotyping data on farm management and additional environmental data. Both variable hatching and resistance development appeared associated with the maintenance of parasite refugia as well as grazing management, particularly reseeding of pasture routinely grazed by young lambs each spring and the practice of set-stocked grazing. Effective quarantine measures were identified as the main protective factor for the development of BZ-resistance whereas set stocked grazing and population bottlenecks, resulting from reseeding heavily contaminated pastures, were risk factors. Spring maximum temperature and other climatic factors were associated with 'typical' hatching of eggs following a chill stimulus whilst several management factors were linked with hatching without prior chilling. For example, practices which reduce parasite numbers on pasture (e.g. re-seeding) or restrict availability of hosts (e.g. resting fields), were found to increase the odds of non-chill hatching. Retention of the timing of lambing and infection level of the host within the fitted model indicated that requirement for a chill stimulus prior to hatching may be plastic, perhaps subject to change throughout the grazing season, in response to immune development or parasite density-dependence within the host. Further investigation of the influence of the factors retained within the fitted models, particularly the theme of parasite refugia which was highlighted in relation to both the presence of BZ-resistance alleles and alternative hatching, is required to establish robust, sustainable parasite control and farm management strategies.

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BACKGROUND AND PURPOSE: The evidence of longitudinal changes in cognition in nasopharyngeal carcinoma (NPC) survivors with radiation-induced brain necrosis (RIBN) after radiotherapy (RT) remained insufficient. We aimed to estimate the clinical progression rate of cognitive decline and identify patients with differential decline rates. MATERIALS AND METHODS: Based on an ongoing prospective cohort study, NPC patients aged ≥18 years old and diagnosed with RIBN were included in this current analysis if they finished the time frame of 3-year follow-up and had at least twice cognition assessments. The Chinese version of the Montreal Cognitive Assessment (MoCA) was used to assess the cognitive state. Linear mixed-effect models were used to analyze the annual progression rates of MoCA total and seven sub-items scores. RESULTS: Among 134 patients in this study, the transition probability from normal to mild/moderate cognitive dysfunction were 14.2 % (19/134) and 1.49 % (2/134) respectively during the median follow-up time of 2.35 years. The total MoCA score declined by -0.569 (SE 0.208) points annually (p = 0.008). Patients with ≤6 years of duration from RT to RIBN have higher annual progression rate of total scores [-0.851 (SE 0.321), p = 0.013; p for interaction = 0.041]. CONCLUSION: Our findings of the annual decline rate of cognition in NPC patients with RIBN from a 3-year longitudinal data, particularly for those who developed RIBN rapidly after RT, have important implications for the upcoming clinical trials designed to prevent or decrease cognitive decline in NPC patients with RIBN, regarding the selection of study patients and the calculation of sample size.

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Background Phenolic compounds, which are widely used as plasticizers, antibacterial agents, and preservatives in industrial production, have endocrine disrupting effects on humans. Previous epidemiological studies on the associations between phenolic compound exposure and blood lipids are mainly based on single measurement of spot urine samples, neglecting potential lag effects of phenolic compounds, and the conclusions are inconsistent. Objective To investigate the effects of short-term exposure to phenolic compounds at different lag days on blood lipid levels in adults. Methods We recruited 143 adults (43 males and 100 females) in Wuhan for three consecutive seasonal rounds of repeated visits: summer and autumn rounds of 2017 and winter of 2018. Morning urine samples were collected for four consecutive days during each round. A set of questionnaires were also distributed on the first day. Physical examinations and fasting venous blood sample collection were conducted on the fourth day. A total of 126 adults were included for analysis (340 person-time, 1251 urine samples). The concentrations of six urinary phenolic compounds [bisphenol A (BPA), bisphenol S (BPS), triclosan (TCS), methyl paraben (MeP), ethyl paraben (EtP), and propyl paraben (PrP)] were determined by ultra-high-performance liquid chromatography-tandem mass spectrometry. Linear mixed-effect models (LMEs), multiple informant models, and generalized linear models were utilized to estimate the associations of urinary phenolic compounds at different lag days with total cholesterol (TC), triglycerides (TG), high density lipoprotein cholesterol (HDL-C), low density lipoprotein cholesterol (LDL-C), and the ratio of TG to HDL-C (TG/HDL-C). Stratified analyses were conducted by selected characteristics. Results After covariate and multiple adjustments, the LMEs indicated that a change in urinary BPA at lag 0 day from the low concentration group (<LOD) to the high concentration group (≥LOD) was associated with a 16.48% (95%CI: 4.41%, 29.94%) increase in TG/HDL-C (P FDR<0.05), and this association was more pronounced in men (P interaction=0.028) and smokers (P interaction=0.040). In addition, a change in urinary TCS at lag 2 day from the low concentration group (<LOD) to the high concentration group (≥LOD) was associated with a 13.22% (95%CI: 3.73%, 23.56%) increase in TG (P FDR<0.05). The positive association of TCS with TG was more evident in subjects aged < 50 years (P interaction=0.037). No significant associations were found between urinary phenolic compounds at other lag days and blood lipids. Conclusion Short-term exposures to BPA and TCS are positively correlated with unfavorable changes in blood lipids in adults, and the association seem to be more pronounced in men, smokers, or individuals aged < 50 years.

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Background: Everolimus is an inhibitor of mammalian target of rapamycin complex 1. As mutations in TSC1 and TSC2, which cause partial-onset seizures associated with TSC, were found in focal cortical dysplasia type Ⅱ (FCD Ⅱ) patients, a clinical trial has been performed to explore the efficacy and safety of everolimus in FCD patients. However, no dosage regimen was determined to treat FCD II. To recommend an optimal dose regimen for FCD patients, a population pharmacokinetic model of everolimus in FCD patients was developed. Methods: The data of everolimus were collected from September 2017 to May 2020 in a tertiary-level hospital in Korea. The model was developed using NONMEM® software version 7.4.1 (Icon Development Solutions, Ellicott City, MD, United States). Results: The population pharmacokinetics of everolimus was described as the one-compartment model with first-order absorption, with the effect of BSA on clearance. The final model was built as follows: TVCL = 12.5 + 9.71 × (BSA/1.5), TVV = 293, and TVKA = 0.585. As a result of simulation, a dose higher than 7 mg/m2 is needed in patients with BSA 0.5 m2, and a dose higher than 6 mg/m2 is needed in patients with BSA 0.7 m2. A dose of 4.5 mg/m2 is enough in the population with BSA higher than 1.5 m2 to meet the target trough range of 5-15 ng/mL. Conclusion: Based on the developed pharmacokinetics model, the optimal dose of everolimus in practice was recommended by considering the available strengths of Afinitor disperz®, 2 mg, 3 mg, and 5 mg.

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Introduction: Under-five mortality rate (U5MR) and maternal mortality rate (MMR) are important indicators for evaluating the quality of perinatal health and child health services in a country or region, and are research priorities for promoting maternal and infant safety and maternal and child health. This paper aimed to analysis and predict the trends of U5MR and MMR in China, to explore the impact of social health services and economic factors on U5MR and MMR, and to provide a basis for relevant departments to formulate relevant policies and measures. Methods: The JoinPoint regression model was established to conduct time trend analysis and describe the trend of neonatal mortality rate (NMR), infant mortality rate (IMR), U5MR and MMR in China from 1991 to 2020. The linear mixed effect model was used to assess the fixed effects of maternal health care services and socioeconomic factors on U5MR and MMR were explored, with year as a random effect to minimize the effect of collinearity. Auto regressive integrated moving average models (ARIMA) were built to predict U5MR and MMR from 2021 to 2025. Results: The NMR, IMR, U5MR and MMR from 1991 to 2020 in China among national, urban and rural areas showed continuous downward trends. The NMR, IMR, U5MR and MMR were significantly negatively correlated with gross domestic product (GDP), the proportion of the total health expenditure (THE) to GDP, system management rate, prenatal care rate, post-natal visit rate and hospital delivery rate. The predicted values of national U5MR from 2021 to 2025 were 7.3 ‰, 7.2 ‰, 7.1 ‰, 7.1 ‰ and 7.2 ‰ and the predicted values of national MMR were 13.8/100000, 12.1/100000, 10.6/100000, 9.6/100000 and 8.3/100000. Conclusion: China has made great achievements in reducing the U5MR and MMR. It is necessary for achieving the goals of Healthy China 2030 by promoting the equalization of basic public health services and further optimizing the allocation of government health resources. China's experience in reducing U5MR and MMR can be used as a reference for developing countries to realize the SDGs.

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