A Bayesian approach to Mendelian randomization using summary statistics in the univariable and multivariable settings with correlated pleiotropy.

Mendelian randomization uses genetic variants as instrumental variables to make causal inferences on the effect of an exposure on an outcome. Due to the recent abundance of high-powered genome-wide association studies, many putative causal exposures of interest have large numbers of independent genetic variants with which they associate, each representing a potential instrument for use in a Mendelian randomization analysis. Such polygenic analyses increase the power of the study design to detect causal effects; however, they also increase the potential for bias due to instrument invalidity. Recent attention has been given to dealing with bias caused by correlated pleiotropy, which results from violation of the "instrument strength independent of direct effect" assumption. Although methods have been proposed that can account for this bias, a number of restrictive conditions remain in many commonly used techniques. In this paper, we propose a Bayesian framework for Mendelian randomization that provides valid causal inference under very general settings. We propose the methods MR-Horse and MVMR-Horse, which can be performed without access to individual-level data, using only summary statistics of the type commonly published by genome-wide association studies, and can account for both correlated and uncorrelated pleiotropy. In simulation studies, we show that the approach retains type I error rates below nominal levels even in high-pleiotropy scenarios. We demonstrate the proposed approaches in applied examples in both univariable and multivariable settings, some with very weak instruments.

An integrative multi-context Mendelian randomization method for identifying risk genes across human tissues.

Mendelian randomization (MR) provides valuable assessments of the causal effect of exposure on outcome, yet the application of conventional MR methods for mapping risk genes encounters new challenges. One of the issues is the limited availability of expression quantitative trait loci (eQTLs) as instrumental variables (IVs), hampering the estimation of sparse causal effects. Additionally, the often context- or tissue-specific eQTL effects challenge the MR assumption of consistent IV effects across eQTL and GWAS data. To address these challenges, we propose a multi-context multivariable integrative MR framework, mintMR, for mapping expression and molecular traits as joint exposures. It models the effects of molecular exposures across multiple tissues in each gene region, while simultaneously estimating across multiple gene regions. It uses eQTLs with consistent effects across more than one tissue type as IVs, improving IV consistency. A major innovation of mintMR involves employing multi-view learning methods to collectively model latent indicators of disease relevance across multiple tissues, molecular traits, and gene regions. The multi-view learning captures the major patterns of disease relevance and uses these patterns to update the estimated tissue relevance probabilities. The proposed mintMR iterates between performing a multi-tissue MR for each gene region and joint learning the disease-relevant tissue probabilities across gene regions, improving the estimation of sparse effects across genes. We apply mintMR to evaluate the causal effects of gene expression and DNA methylation for 35 complex traits using multi-tissue QTLs as IVs. The proposed mintMR controls genome-wide inflation and offers insights into disease mechanisms.

ReProMSig: an integrative platform for development and application of reproducible multivariable models for cancer prognosis supporting guideline-based transparent reporting.

Adequate reporting is essential for evaluating the performance and clinical utility of a prognostic prediction model. Previous studies indicated a prevalence of incomplete or suboptimal reporting in translational and clinical studies involving development of multivariable prediction models for prognosis, which limited the potential applications of these models. While reporting templates introduced by the established guidelines provide an invaluable framework for reporting prognostic studies uniformly, there is a widespread lack of qualified adherence, which may be due to miscellaneous challenges in manual reporting of extensive model details, especially in the era of precision medicine. Here, we present ReProMSig (Reproducible Prognosis Molecular Signature), a web-based integrative platform providing the analysis framework for development, validation and application of a multivariable prediction model for cancer prognosis, using clinicopathological features and/or molecular profiles. ReProMSig platform supports transparent reporting by presenting both methodology details and analysis results in a strictly structured reporting file, following the guideline checklist with minimal manual input needed. The generated reporting file can be published together with a developed prediction model, to allow thorough interrogation and external validation, as well as online application for prospective cases. We demonstrated the utilities of ReProMSig by development of prognostic molecular signatures for stage II and III colorectal cancer respectively, in comparison with a published signature reproduced by ReProMSig. Together, ReProMSig provides an integrated framework for development, evaluation and application of prognostic/predictive biomarkers for cancer in a more transparent and reproducible way, which would be a useful resource for health care professionals and biomedical researchers.

Using mindfulness-based intervention to promote executive function in young children: a multivariable and multiscale sample entropy study.

Early childhood marks a pivotal period in the maturation of executive function, the cognitive ability to consciously regulate actions and thoughts. Mindfulness-based interventions have shown promise in bolstering executive function in children. This study used the functional near-infrared spectroscopy technique to explore the impact of mindfulness-based training on young children. Brain imaging data were collected from 68 children (41 boys, aged 61.8 ± 10.7 months) who were randomly assigned to either an intervention group (N = 37, aged 60.03 ± 11.14 months) or a control group (N = 31, aged 59.99 ± 10.89 months). Multivariate and multiscale sample entropy analyses were used. The results showed that: (1) brain complexity was reduced in the intervention group after receiving the mindfulness-based intervention in all three executive function tasks (ps < 0.05), indicating a more efficient neural processing mechanism after the intervention; (2) difference comparisons between the intervention and control groups showed significant differences in relevant brain regions during cognitive shifting (left dorsolateral prefrontal cortex and medial prefrontal cortex) and working memory tasks (left dorsolateral prefrontal cortex), which corroborates with improved behavioral results in the intervention group (Z = -3.674, P < 0.001 for cognitive shifting; Z = 2.594, P < 0.01 for working memory). These findings improve our understanding of early brain development in young children and highlight the neural mechanisms by which mindfulness-based interventions affect executive function. Implications for early intervention to promote young children's brain development are also addressed.

Multi-variable AUC for sifting complementary features and its biomedical application.

Although sifting functional genes has been discussed for years, traditional selection methods tend to be ineffective in capturing potential specific genes. First, typical methods focus on finding features (genes) relevant to class while irrelevant to each other. However, the features that can offer rich discriminative information are more likely to be the complementary ones. Next, almost all existing methods assess feature relations in pairs, yielding an inaccurate local estimation and lacking a global exploration. In this paper, we introduce multi-variable Area Under the receiver operating characteristic Curve (AUC) to globally evaluate the complementarity among features by employing Area Above the receiver operating characteristic Curve (AAC). Due to AAC, the class-relevant information newly provided by a candidate feature and that preserved by the selected features can be achieved beyond pairwise computation. Furthermore, we propose an AAC-based feature selection algorithm, named Multi-variable AUC-based Combined Features Complementarity, to screen discriminative complementary feature combinations. Extensive experiments on public datasets demonstrate the effectiveness of the proposed approach. Besides, we provide a gene set about prostate cancer and discuss its potential biological significance from the machine learning aspect and based on the existing biomedical findings of some individual genes.

Identifying the critical states of complex diseases by the dynamic change of multivariate distribution.

The dynamics of complex diseases are not always smooth; they are occasionally abrupt, i.e. there is a critical state transition or tipping point at which the disease undergoes a sudden qualitative shift. There are generally a few significant differences in the critical state in terms of gene expressions or other static measurements, which may lead to the failure of traditional differential expression-based biomarkers to identify such a tipping point. In this study, we propose a computational method, the direct interaction network-based divergence, to detect the critical state of complex diseases by exploiting the dynamic changes in multivariable distributions inferred from observable samples and local biomolecular direct interaction networks. Such a method is model-free and applicable to both bulk and single-cell expression data. Our approach was validated by successfully identifying the tipping point just before the occurrence of a critical transition for both a simulated data set and seven real data sets, including those from The Cancer Genome Atlas and two single-cell RNA-sequencing data sets of cell differentiation. Functional and pathway enrichment analyses also validated the computational results from the perspectives of both molecules and networks.

Metabolites Associated With Uremic Symptoms in Patients With CKD: Findings From the Chronic Renal Insufficiency Cohort (CRIC) Study.

RATIONALE & OBJECTIVE: The toxins that contribute to uremic symptoms in patients with chronic kidney disease (CKD) are unknown. We sought to apply complementary statistical modeling approaches to data from untargeted plasma metabolomic profiling to identify solutes associated with uremic symptoms in patients with CKD. STUDY DESIGN: Cross-sectional. SETTING & PARTICIPANTS: 1,761 Chronic Renal Insufficiency Cohort (CRIC) participants with CKD not treated with dialysis. PREDICTORS: Measurement of 448 known plasma metabolites. OUTCOMES: The uremic symptoms of fatigue, anorexia, pruritus, nausea, paresthesia, and pain were assessed by single items on the Kidney Disease Quality of Life-36 instrument. ANALYTICAL APPROACH: Multivariable adjusted linear regression, least absolute shrinkage and selection operator linear regression, and random forest models were used to identify metabolites associated with symptom severity. After adjustment for multiple comparisons, metabolites selected in at least 2 of the 3 modeling approaches were deemed "overall significant." RESULTS: Participant mean estimated glomerular filtration rate was 43mL/min/1.73m2, with 44% self-identifying as female and 41% as non-Hispanic Black. The prevalence of uremic symptoms ranged from 22% to 55%. We identified 17 metabolites for which a higher level was associated with greater severity of at least one uremic symptom and 9 metabolites inversely associated with uremic symptom severity. Many of these metabolites exhibited at least a moderate correlation with estimated glomerular filtration rate (Pearson's r≥0.5), and some were also associated with the risk of developing kidney failure or death in multivariable adjusted Cox regression models. LIMITATIONS: Lack of a second independent cohort for external validation of our findings. CONCLUSIONS: Metabolomic profiling was used to identify multiple solutes associated with uremic symptoms in adults with CKD, but future validation and mechanistic studies are needed. PLAIN-LANGUAGE SUMMARY: Individuals living with chronic kidney disease (CKD) often experience symptoms related to CKD, traditionally called uremic symptoms. It is likely that CKD results in alterations in the levels of numerous circulating substances that, in turn, cause uremic symptoms; however, the identity of these solutes is not known. In this study, we used metabolomic profiling in patients with CKD to gain insights into the pathophysiology of uremic symptoms. We identified 26 metabolites whose levels were significantly associated with at least one of the symptoms of fatigue, anorexia, itchiness, nausea, paresthesia, and pain. The results of this study lay the groundwork for future research into the biological causes of symptoms in patients with CKD.

COVID-19 in three waves in a tertiary referral hospital in Belgium: a comparison of patient characteristics, management, and outcome.

PURPOSE: Few studies have compared patient characteristics, clinical management, and outcome of patients with COVID-19 between the different epidemic waves. In this study, we describe patient characteristics, treatment, and outcome of patients admitted for COVID-19 in the Antwerp University Hospital over the first three epidemic waves of 2020-2021. METHODS: Retrospective observational study of COVID-19 patients in a Belgian tertiary referral hospital. All adult patients with COVID-19, hospitalized between February 29, 2020, and June 30, 2021, were included. Standardized routine medical data was collected from patient records. Risk factors were assessed with multivariable logistic regression. RESULTS: We included 722 patients, during the first (n = 179), second (n = 347) and third (n = 194) wave. We observed the lowest disease severity at admission during the first wave, and more elderly and comorbid patients during the second wave. Throughout the subsequent waves we observed an increasing use of corticosteroids and high-flow oxygen therapy. In spite of increasing number of complications throughout the subsequent waves, mortality decreased each wave (16.6%,15.6% 11.9% in 1st, 2nd and 3rd wave respectively). C-reactive protein above 150 mg/L was predictive for the need for intensive care unit admission (odds ratio (OR) 3.77, 95% confidence interval (CI) 2.32-6.15). A Charlson comorbidity index ≥ 5 (OR 5.68, 95% CI 2.54-12.70) and interhospital transfers (OR 3.78, 95% CI 2.05-6.98) were associated with a higher mortality. CONCLUSIONS: We observed a reduction in mortality each wave, despite increasing comorbidity. Evolutions in patient management such as high-flow oxygen therapy on regular wards and corticosteroid use may explain this favorable evolution.

Sex diversity in the 21st century: Concepts, frameworks, and approaches for the future of neuroendocrinology.

Sex is ubiquitous and variable throughout the animal kingdom. Historically, scientists have used reductionist methodologies that rely on a priori sex categorizations, in which two discrete sexes are inextricably linked with gamete type. However, this binarized operationalization does not adequately reflect the diversity of sex observed in nature. This is due, in part, to the fact that sex exists across many levels of biological analysis, including genetic, molecular, cellular, morphological, behavioral, and population levels. Furthermore, the biological mechanisms governing sex are embedded in complex networks that dynamically interact with other systems. To produce the most accurate and scientifically rigorous work examining sex in neuroendocrinology and to capture the full range of sex variability and diversity present in animal systems, we must critically assess the frameworks, experimental designs, and analytical methods used in our research. In this perspective piece, we first propose a new conceptual framework to guide the integrative study of sex. Then, we provide practical guidance on research approaches for studying sex-associated variables, including factors to consider in study design, selection of model organisms, experimental methodologies, and statistical analyses. We invite fellow scientists to conscientiously apply these modernized approaches to advance our biological understanding of sex and to encourage academically and socially responsible outcomes of our work. By expanding our conceptual frameworks and methodological approaches to the study of sex, we will gain insight into the unique ways that sex exists across levels of biological organization to produce the vast array of variability and diversity observed in nature.

New labor curves of dilation and station to improve the accuracy of predicting labor progress.

BACKGROUND: The diagnosis of failure to progress, the most common indication for intrapartum cesarean delivery, is based on the assessment of cervical dilation and station over time. Labor curves serve as references for expected changes in dilation and fetal descent. The labor curves of Friedman, Zhang et al, and others are based on time alone and derived from mothers with spontaneous labor onset. However, labor induction is now common, and clinicians also consider other factors when assessing labor progress. Labor curves that consider the use of labor induction and other factors that influence labor progress have the potential to be more accurate and closer to clinical decision-making. OBJECTIVE: This study aimed to compare the prediction errors of labor curves based on a single factor (time) or multiple clinically relevant factors using two modeling methods: mixed-effects regression, a standard statistical method, and Gaussian processes, a machine learning method. STUDY DESIGN: This was a longitudinal cohort study of changes in dilation and station based on data from 8022 births in nulliparous women with a live, singleton, vertex-presenting fetus ≥35 weeks of gestation with a vaginal delivery. New labor curves of dilation and station were generated with 10-fold cross-validation. External validation was performed using a geographically independent group. Model variables included time from the first examination in the 20 hours before delivery; dilation, effacement, and station recorded at the previous examination; cumulative contraction counts; and use of epidural anesthesia and labor induction. To assess model accuracy, differences between each model's predicted value and its corresponding observed value were calculated. These prediction errors were summarized using mean absolute error and root mean squared error statistics. RESULTS: Dilation curves based on multiple parameters were more accurate than those derived from time alone. The mean absolute error of the multifactor methods was better (lower) than those of the single-factor methods (0.826 cm [95% confidence interval, 0.820-0.832] for the multifactor machine learning and 0.893 cm [95% confidence interval, 0.885-0.901] for the multifactor mixed-effects method and 2.122 cm [95% confidence interval, 2.108-2.136] for the single-factor methods; P<.0001 for both comparisons). The root mean squared errors of the multifactor methods were also better (lower) than those of the single-factor methods (1.126 cm [95% confidence interval, 1.118-1.133] for the machine learning [P<.0001] and 1.172 cm [95% confidence interval, 1.164-1.181] for the mixed-effects methods and 2.504 cm [95% confidence interval, 2.487-2.521] for the single-factor [P<.0001 for both comparisons]). The multifactor machine learning dilation models showed small but statistically significant improvements in accuracy compared to the mixed-effects regression models (P<.0001). The multifactor machine learning method produced a curve of descent with a mean absolute error of 0.512 cm (95% confidence interval, 0.509-0.515) and a root mean squared error of 0.660 cm (95% confidence interval, 0.655-0.666). External validation using independent data produced similar findings. CONCLUSION: Cervical dilation models based on multiple clinically relevant parameters showed improved (lower) prediction errors compared to models based on time alone. The mean prediction errors were reduced by more than 50%. A more accurate assessment of departure from expected dilation and station may help clinicians optimize intrapartum management.

Men's Satisfaction with General Health Services is Associated with Future Use of HIV Testing in Malawi: A Community-Representative Survey.

Across sub-Saharan Africa, men are less likely to know their HIV status than women, leading to later treatment initiation. Little is known about how experiences with general health services affect men's use of HIV testing. We used data from a 2019 community-representative survey of men in Malawi to understand frequency and cause of men's negative health service experiences (defined as men reporting they "would not recommend" a facility) and their association with future HIV testing. We conducted univariable and multivariable logistic regressions to determine which aspects of health facility visits were associated with would-not-recommend experiences and to determine if would-not-recommend experiences 12-24 months prior to the survey were associated with HIV testing in the 12 months prior to the survey. Among 1,098 men eligible for HIV testing in the 12 months prior to the survey, median age was 34 years; 9% of men reported at least one would-not-recommend experience, which did not differ by sociodemographics, gender norm beliefs, or HIV stigma beliefs. The factors most strongly associated with would-not-recommend experiences were cost (aOR 5.8, 95%CI 2.9-11.4), cleanliness (aOR 4.2, 95%CI 1.8-9.9), medicine availability (aOR 3.3, 95%CI 1.7-6.4), and wait times (aOR 2.7, 95%CI 1.5-5.0). Reporting a would-not-recommend experience 12-24 months ago was associated with a 59% decrease in likelihood of testing for HIV in the last 12 months (aOR 0.41; 95% CI:0.17-0.96). Dissatisfaction with general health services was strongly associated with reduced HIV testing. Coverage of high-priority screening services like HIV testing may benefit from improving overall health system quality.

Clinical and health care utilization variables can predict 90-day hospital re-admission in adults with Crohn's disease for point of care risk evaluation.

BACKGROUND: Hospital re-admission for persons with Crohn's disease (CD) is a significant contributor to morbidity and healthcare costs. We derived prediction models of risk of 90-day re-hospitalization among persons with CD that could be applied at hospital discharge to target outpatient interventions mitigating this risk. METHODS: We performed a retrospective study in persons with CD admitted between 2009 and 2016 for an acute CD-related indication. Demographic, clinical, and health services predictor variables were ascertained through chart review and linkage to administrative health databases. We derived and internally validated a multivariable logistic regression model of 90-day CD-related re-hospitalization. We selected the optimal probability cut-point to maximize Youden's index. RESULTS: There were 524 CD hospitalizations and 57 (10.9%) CD re-hospitalizations within 90 days of discharge. Our final model included hospitalization within the prior year (adjusted odds ratio [aOR] 3.27, 95% confidence interval [CI] 1.76-6.08), gastroenterologist consultation within the prior year (aOR 0.185, 95% CI 0.0950-0.360), intra-abdominal surgery during index hospitalization (aOR 0.216, 95% CI 0.0500-0.934), and new diagnosis of CD during index hospitalization (aOR 0.327, 95% CI 0.0950-1.13). The model demonstrated good discrimination (optimism-corrected c-statistic value 0.726) and excellent calibration (Hosmer-Lemeshow goodness-of-fit p-value 0.990). The optimal model probability cut point allowed for a sensitivity of 71.9% and specificity of 70.9% for identifying 90-day re-hospitalization, at a false positivity rate of 29.1% and false negativity rate of 28.1%. CONCLUSIONS: Demographic, clinical, and health services variables can help discriminate persons with CD at risk of early re-hospitalization, which could permit targeted post-discharge intervention.

Nasopharyngeal angiotensin converting enzyme 2 (ACE2) expression as a risk-factor for SARS-CoV-2 transmission in concurrent hospital associated outbreaks.

BACKGROUND: Widespread human-to-human transmission of the severe acute respiratory syndrome coronavirus two (SARS-CoV-2) stems from a strong affinity for the cellular receptor angiotensin converting enzyme two (ACE2). We investigate the relationship between a patient's nasopharyngeal ACE2 transcription and secondary transmission within a series of concurrent hospital associated SARS-CoV-2 outbreaks in British Columbia, Canada. METHODS: Epidemiological case data from the outbreak investigations was merged with public health laboratory records and viral lineage calls, from whole genome sequencing, to reconstruct the concurrent outbreaks using infection tracing transmission network analysis. ACE2 transcription and RNA viral load were measured by quantitative real-time polymerase chain reaction. The transmission network was resolved to calculate the number of potential secondary cases. Bivariate and multivariable analyses using Poisson and Negative Binomial regression models was performed to estimate the association between ACE2 transcription the number of SARS-CoV-2 secondary cases. RESULTS: The infection tracing transmission network provided n = 76 potential transmission events across n = 103 cases. Bivariate comparisons found that on average ACE2 transcription did not differ between patients and healthcare workers (P = 0.86). High ACE2 transcription was observed in 98.6% of transmission events, either the primary or secondary case had above average ACE2. Multivariable analysis found that the association between ACE2 transcription (log2 fold-change) and the number of secondary transmission events differs between patients and healthcare workers. In health care workers Negative Binomial regression estimated that a one-unit change in ACE2 transcription decreases the number of secondary cases (ß = -0.132 (95%CI: -0.255 to -0.0181) adjusting for RNA viral load. Conversely, in patients a one-unit change in ACE2 transcription increases the number of secondary cases (ß = 0.187 (95% CI: 0.0101 to 0.370) adjusting for RNA viral load. Sensitivity analysis found no significant relationship between ACE2 and secondary transmission in health care workers and confirmed the positive association among patients. CONCLUSION: Our study suggests that ACE2 transcription has a positive association with SARS-CoV-2 secondary transmission in admitted inpatients, but not health care workers in concurrent hospital associated outbreaks, and it should be further investigated as a risk-factor for viral transmission.

Impact of the pulmonary ventilation function on the prognosis of suspected asthma patients: a retrospective observational study.

OBJECTIVE: Asthma is a common chronic respiratory diseases, and the relationship between pulmonary ventilation function and the prognosis of patients with suspected asthma is not well understood. This study aims to explore the impact of pulmonary ventilation functions on the prognosis of patients with suspected asthma. METHODS: This retrospective observational study included patients with suspected asthma who were diagnosed and treated at the Guangdong Provincial Hospital of Traditional Chinese Medicine between August 2015 and January 2020. The primary outcome of interest was improvement in asthma symptoms, as measured by bronchial provocation test (BPT) results within one year after diagnosis. The impact of pulmonary ventilation functions on prognosis was explored by multivariable logistic regression analysis. RESULTS: Seventy-two patients were included in the study. Patients with normal (OR = 0.123, p = .004) or generally normal (OR = 0.075, p = .039) pulmonary ventilation function were more likely to achieve improvement in asthma symptoms compared with patients with mild obstruction. There were no significant differences between the improvement and non-improvement groups in baseline characteristics. CONCLUSION: These results suggest that suspected asthma patients with normal or generally normal pulmonary ventilation function are more likely to achieve improvement in asthma symptoms within one year compared to patients with mild obstruction.

Predictive factors and oncological outcomes of pathological T3a upstaging in patients with clinical T1 renal cell carcinoma undergoing partial nephrectomy.

OBJECTIVES: To investigate predictive factors and oncological outcomes of pathological T3a upstaging in renal cell carcinoma patients who were initially diagnosed as clinical T1 and treated with partial nephrectomy. METHODS AND MATERIALS: The clinical records and survival data of 1617 patients, who had undergone partial nephrectomy for clinical T1 renal cell carcinoma at Tokyo Women's Medical University, Tokyo, Japan between January 2011 and December 2020, were analyzed retrospectively. RESULTS: Of 1617 clinical T1 renal cell carcinoma patients who underwent partial nephrectomy, 28 (1.73%) had pathological T3a upstaging. In the multivariable analysis for pathological T3a upstaging using logistic regression models, male sex and clinical T1b were significant factors associated with pathological T3a upstaging (male sex: odds ratio = 5.07, 95% confidence interval: 1.18-21.8, clinical T1b: odds ratio = 8.36, 95% confidence interval: 3.56-19.6). The Kaplan-Meier method of the recurrence-free survival showed shorter recurrence-free survival in patients with pathological T3a upstaging than in those with pathological T1 (P < 0.0001). In the multivariable analysis using Cox proportional hazards regression models, pathological T3a upstaging was no longer significantly associated with recurrence-free survival after adjustment for other pathological factors (hazard ratio = 1.59, 95% confidence interval: 0.58-4.36). In a sensitivity analysis that analyzed its components individually instead of whole pathological T3a, neither perinephric fat invasion, sinus fat invasion, nor renal vein invasion was associated with recurrence-free survival. CONCLUSIONS: Male sex and clinical T1b were significant predictors for pathological T3a upstaging after partial nephrectomy in clinical T1 renal cell carcinoma patients. Although patients with pathological T3a upstaging had worse recurrence-free survival compared with those without upstaging, multivariable analyses revealed that pathological T3a upstaging was not an independent predictor for poor recurrence-free survival.

Decreased frontal lobe complexity in left-behind children during joint attention: a fNIRS study with multivariable and multiscale sample entropy analysis.

Human brain development is shaped by experiences, especially during preschool, the critical period for cognitive and socioemotional development. This study employed the functional Near-Infrared Spectroscopy technique to explore the neural differences between left-behind children (LBC) and non-left-behind children (NLBC) on joint attention. Through collecting brain image data of 50 children (26 boys, aged 65.08 ± 6.28 months) and conducting multivariable and multiscale sample entropy (MMSE) analysis, the present study found that: (i) LBC showed lower brain complexity than NLBC in right prefrontal cortex; (ii) all participants demonstrated higher brain complexity in responding to joint attention conditions, compared to initiating joint attention ones; (iii) their brain complexity during joint attention was negatively associated with their emotional abilities. The findings advance our understanding of early brain development in LBC by providing evidence for the neural process characteristics of joint attention. Implications for early intervention to promote their brain development are also addressed.

Edge-centric functional network predicts risk propensity in economic decision-making: evidence from a resting-state fMRI study.

Despite node-centric studies revealing an association between resting-state functional connectivity and individual risk propensity, the prediction of future risk decisions remains undetermined. Herein, we applied a recently emerging edge-centric method, the edge community similarity network (ECSN), to alternatively describe the community structure of resting-state brain activity and to probe its contribution to predicting risk propensity during gambling. Results demonstrated that inter-individual variability of risk decisions correlates with the inter-subnetwork couplings spanning the visual network (VN) and default mode network (DMN), cingulo-opercular task control network, and sensory/somatomotor hand network (SSHN). Particularly, participants who have higher community similarity of these subnetworks during the resting state tend to choose riskier and higher yielding bets. And in contrast to low-risk propensity participants, those who behave high-risky show stronger couplings spanning the VN and SSHN/DMN. Eventually, based on the resting-state ECSN properties, the risk rate during the gambling task is effectively predicted by the multivariable linear regression model at the individual level. These findings provide new insights into the neural substrates of the inter-individual variability in risk propensity and new neuroimaging metrics to predict individual risk decisions in advance.

Neonatal phototherapy and risk of epilepsy-A Danish population based study.

To evaluate the risk of epilepsy in children who received neonatal phototherapy. A cohort of live singletons born at a Danish hospital (2002-2016) with a gestational age ≥ 35 weeks. We used Cox regression to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) of epilepsy in children treated with neonatal phototherapy compared to children not treated with neonatal phototherapy in the general population, and in a subpopulation of children who had serum bilirubin measurement. Adjusted HRs (aHR) were computed using multivariable and propensity score matching models to take maternal and neonatal factors into consideration. Children were followed from day 29 after birth to diagnosis of epilepsy, death, emigration, or December 31, 2016. Among 65,365 children, 958 (1.5%) received neonatal phototherapy. Seven children (incidence rates (IRs): 10.8 /10,000 person-years) who received neonatal phototherapy and 354 children (IR: 7.7) who did not receive neonatal phototherapy were diagnosed with epilepsy. Neonatal phototherapy was not associated with an increased risk of epilepsy using the multivariable (aHR 0.95, 95% CI: 0.43-2.09) and propensity score matched (aHR 0.94, 95% CI: 0.39-2.28) models. In the subpopulation of 9,378 children with bilirubin measurement, 928 (9.9%) received neonatal phototherapy. In the analysis of the subpopulation in which bilirubin level and age at the time of bilirubin measurement were further taking into consideration, neonatal phototherapy was not associated with an increased risk of epilepsy using the multivariable (aHR 1.26, 95% CI: 0.54-2.97) and propensity score matched (aHR 1.24, 95% CI: 0.47-3.25) models,Conclusions: Neonatal phototherapy was not associated with an increased risk of epilepsy after taking maternal and neonatal factors into consideration. What is known: • A few studies have suggested that neonatal phototherapy for hyperbilirubinemia may increase the risk of childhood epilepsy. • Whether the observed associations contribute to hyperbilirubinemia, phototherapy, or underlying factors requires further investigation. What is new: • This study revealed no increased risk of epilepsy in children treated with neonatal phototherapy compared to children not treated with phototherapy after taking maternal and neonatal factors into consideration. • After further taking bilirubin level and age at the time of bilirubin measurement into consideration, neonatal phototherapy was not associated with an increased risk of epilepsy.

Association Between Atopic Dermatitis in Pregnant Women and Preterm Births: The Japan Environment and Children's Study.

OBJECTIVES: This study aimed to evaluate the association between atopic dermatitis in pregnant women and preterm births, accounting for maternal ritodrine hydrochloride administration status. METHODS: Data of 83,796 women with singleton pregnancies at and after 22 weeks of gestation (enrolled between 2011 and 2014) were analyzed. These data were obtained from the Japan Environment and Children's Study. Atopic dermatitis was defined based on self-reported questionnaire responses obtained during the first trimester. The primary outcome measures were preterm births before 37, 32, and 28 weeks of gestation. Using a multivariable logistic regression model, odds ratios for preterm births in pregnant women with atopic dermatitis were calculated, with women without atopic dermatitis included in the reference group. This analysis considered confounding factors and maternal ritodrine hydrochloride administration. RESULTS: Among pregnant women with atopic dermatitis, the adjusted odds ratios (95% confidence intervals) for preterm births before 37, 32, and 28 weeks of gestation were 0.89 (0.81-0.98), 0.98 (0.74-1.30), and 0.88 (0.50-1.55), respectively. This trend remained consistent after excluding participants who received ritodrine hydrochloride. CONCLUSIONS FOR PRACTICE: Atopic dermatitis in pregnant women was significantly associated with a decreased incidence of preterm births before 37 weeks of gestation, even after accounting for the effects of maternal ritodrine hydrochloride administration.

Pediatric and Young Adult Household Transmission of the Initial Waves of SARS-CoV-2 in the United States: Administrative Claims Study.

BACKGROUND: The correlates responsible for the temporal changes of intrahousehold SARS-CoV-2 transmission in the United States have been understudied mainly due to a lack of available surveillance data. Specifically, early analyses of SARS-CoV-2 household secondary attack rates (SARs) were small in sample size and conducted cross-sectionally at single time points. From these limited data, it has been difficult to assess the role that different risk factors have had on intrahousehold disease transmission in different stages of the ongoing COVID-19 pandemic, particularly in children and youth. OBJECTIVE: This study aimed to estimate the transmission dynamic and infectivity of SARS-CoV-2 among pediatric and young adult index cases (age 0 to 25 years) in the United States through the initial waves of the pandemic. METHODS: Using administrative claims, we analyzed 19 million SARS-CoV-2 test records between January 2020 and February 2021. We identified 36,241 households with pediatric index cases and calculated household SARs utilizing complete case information. Using a retrospective cohort design, we estimated the household SARS-CoV-2 transmission between 4 index age groups (0 to 4 years, 5 to 11 years, 12 to 17 years, and 18 to 25 years) while adjusting for sex, family size, quarter of first SARS-CoV-2 positive record, and residential regions of the index cases. RESULTS: After filtering all household records for greater than one member in a household and missing information, only 36,241 (0.85%) of 4,270,130 households with a pediatric case remained in the analysis. Index cases aged between 0 and 17 years were a minority of the total index cases (n=11,484, 11%). The overall SAR of SARS-CoV-2 was 23.04% (95% CI 21.88-24.19). As a comparison, the SAR for all ages (0 to 65+ years) was 32.4% (95% CI 32.1-32.8), higher than the SAR for the population between 0 and 25 years of age. The highest SAR of 38.3% was observed in April 2020 (95% CI 31.6-45), while the lowest SAR of 15.6% was observed in September 2020 (95% CI 13.9-17.3). It consistently decreased from 32% to 21.1% as the age of index groups increased. In a multiple logistic regression analysis, we found that the youngest pediatric age group (0 to 4 years) had 1.69 times (95% CI 1.42-2.00) the odds of SARS-CoV-2 transmission to any family members when compared with the oldest group (18 to 25 years). Family size was significantly associated with household viral transmission (odds ratio 2.66, 95% CI 2.58-2.74). CONCLUSIONS: Using retrospective claims data, the pediatric index transmission of SARS-CoV-2 during the initial waves of the COVID-19 pandemic in the United States was associated with location and family characteristics. Pediatric SAR (0 to 25 years) was less than the SAR for all age other groups. Less than 1% (n=36,241) of all household data were retained in the retrospective study for complete case analysis, perhaps biasing our findings. We have provided measures of baseline household pediatric transmission for tracking and comparing the infectivity of later SARS-CoV-2 variants.