The role of circadian rhythms and sleep in the aetiology of autism spectrum disorder and attention-deficit/hyperactivity disorder: New evidence from bidirectional two-sample Mendelian randomization analysis.

LAY ABSTRACT: Research shows that people with autism spectrum disorder and attention-deficit/hyperactivity disorder often have sleep issues and problems with the body's natural daily rhythms, known as circadian rhythms. By exploring the genetic variants associated with these rhythms and the conditions, this study reveals that these rhythm changes and sleep patterns are directly linked to autism spectrum disorder and attention-deficit/hyperactivity disorder. It found that the timing of one's most active hours can increase the likelihood of having both autism spectrum disorder and attention-deficit/hyperactivity disorder. Importantly, it also shows that good sleep quality might protect against autism spectrum disorder, while disturbed sleep in people with attention-deficit/hyperactivity disorder seems to be a result rather than the cause of the condition. This understanding can help doctors and researchers develop better treatment approaches that focus on the specific ways sleep and body rhythms affect those with autism spectrum disorder and attention-deficit/hyperactivity disorder, considering their unique associations with circadian rhythms and sleep patterns. Understanding these unique links can lead to more effective, personalized care for those affected by these conditions.

Autologous transplantation of P63+ lung progenitor cells for chronic obstructive pulmonary disease therapy.

Adult lung resident stem/progenitor cells, including P63+ progenitor cells, have demonstrated the capacity for regeneration of lung epithelium in preclinical models. Here, we report a clinical trial of intrapulmonary P63+ progenitor cell transplantation in 28 participants with stage II to IV chronic obstructive pulmonary disease (COPD). Autologous P63+ progenitor cells were isolated from the airway basal layer of participants in the intervention group via bronchoscopic brushing, cultured for 3 to 5 weeks, and then transplanted back into the lungs via bronchoscopy at 0.7 × 106 to 5.2 × 106 cells per kilogram of body weight. Twenty patients were evaluable at the end of the study (intervention group, n = 17; control group, n = 3). No grade 3 to 5 adverse events (AEs) or serious AEs occurred. Although bronchoscopy-associated AEs were recorded in participants in the intervention group, other AEs were not substantial different between groups. Twenty-four weeks after transplantation, participants in the intervention group displayed improvement in gas transfer capacity [diffusing capacity of the lung for carbon monoxide (DLCO) change from baseline: +18.2%], whereas the control group experienced a decrease (DLCO change from baseline: -17.4%; P = 0.008). Furthermore, participants in the intervention group showed >30-meter increase in walking distance within 6 minutes. Transcriptomic analysis of progenitor cells isolated from responding and nonresponding individuals in the intervention group showed that higher expression of P63 was associated with treatment efficacy. In conclusion, transplantation of cultured P63+ lung progenitor cells was safe and might represent a potential therapeutic strategy for COPD.

A Bayesian framework for modeling COVID-19 case numbers through longitudinal monitoring of SARS-CoV-2 RNA in wastewater.

Wastewater-based surveillance has become an important tool for research groups and public health agencies investigating and monitoring the COVID-19 pandemic and other public health emergencies including other pathogens and drug abuse. While there is an emerging body of evidence exploring the possibility of predicting COVID-19 infections from wastewater signals, there remain significant challenges for statistical modeling. Longitudinal observations of viral copies in municipal wastewater can be influenced by noisy datasets and missing values with irregular and sparse samplings. We propose an integrative Bayesian framework to predict daily positive cases from weekly wastewater observations with missing values via functional data analysis techniques. In a unified procedure, the proposed analysis models severe acute respiratory syndrome coronavirus-2 RNA wastewater signals as a realization of a smooth process with error and combines the smooth process with COVID-19 cases to evaluate the prediction of positive cases. We demonstrate that the proposed framework can achieve these objectives with high predictive accuracies through simulated and observed real data.

Campus node-based wastewater surveillance enables COVID-19 case localization and confirms lower SARS-CoV-2 burden relative to the surrounding community.

Wastewater-based surveillance (WBS) has been established as a powerful tool that can guide health policy at multiple levels of government. However, this approach has not been well assessed at more granular scales, including large work sites such as University campuses. Between August 2021 and April 2022, we explored the occurrence of SARS-CoV-2 RNA in wastewater using qPCR assays from multiple complimentary sewer catchments and residential buildings spanning the University of Calgary's campus and how this compared to levels from the municipal wastewater treatment plant servicing the campus. Real-time contact tracing data was used to evaluate an association between wastewater SARS-CoV-2 burden and clinically confirmed cases and to assess the potential of WBS as a tool for disease monitoring across worksites. Concentrations of wastewater SARS-CoV-2 N1 and N2 RNA varied significantly across six sampling sites - regardless of several normalization strategies - with certain catchments consistently demonstrating values 1-2 orders higher than the others. Relative to clinical cases identified in specific sewersheds, WBS provided one-week leading indicator. Additionally, our comprehensive monitoring strategy enabled an estimation of the total burden of SARS-CoV-2 for the campus per capita, which was significantly lower than the surrounding community (p≤0.001). Allele-specific qPCR assays confirmed that variants across campus were representative of the community at large, and at no time did emerging variants first debut on campus. This study demonstrates how WBS can be efficiently applied to locate hotspots of disease activity at a very granular scale, and predict disease burden across large, complex worksites.

Wastewater-based surveillance can be used to model COVID-19-associated workforce absenteeism.

Wastewater-based surveillance (WBS) of infectious diseases is a powerful tool for understanding community COVID-19 disease burden and informing public health policy. The potential of WBS for understanding COVID-19's impact in non-healthcare settings has not been explored to the same degree. Here we examined how SARS-CoV-2 measured from municipal wastewater treatment plants (WWTPs) correlates with workforce absenteeism. SARS-CoV-2 RNA N1 and N2 were quantified three times per week by RT-qPCR in samples collected at three WWTPs servicing Calgary and surrounding areas, Canada (1.4 million residents) between June 2020 and March 2022. Wastewater trends were compared to workforce absenteeism using data from the largest employer in the city (>15,000 staff). Absences were classified as being COVID-19-related, COVID-19-confirmed, and unrelated to COVID-19. Poisson regression was performed to generate a prediction model for COVID-19 absenteeism based on wastewater data. SARS-CoV-2 RNA was detected in 95.5 % (85/89) of weeks assessed. During this period 6592 COVID-19-related absences (1896 confirmed) and 4524 unrelated absences COVID-19 cases were recorded. A generalized linear regression using a Poisson distribution was performed to predict COVID-19-confirmed absences out of the total number of absent employees using wastewater data as a leading indicator (P < 0.0001). The Poisson regression with wastewater as a one-week leading signal has an Akaike information criterion (AIC) of 858, compared to a null model (excluding wastewater predictor) with an AIC of 1895. The likelihood-ratio test comparing the model with wastewater signal with the null model shows statistical significance (P < 0.0001). We also assessed the variation of predictions when the regression model was applied to new data, with the predicted values and corresponding confidence intervals closely tracking actual absenteeism data. Wastewater-based surveillance has the potential to be used by employers to anticipate workforce requirements and optimize human resource allocation in response to trackable respiratory illnesses like COVID-19.

A Bayesian genomic selection approach incorporating prior feature ordering and population structures with application to coronary artery disease.

Coronary artery disease is one of the most common types of cardiovascular disease. Death from coronary heart disease is influenced by genetic factors in both women and men. In this article, we propose a novel Bayesian variable selection framework for the identification of important genetic variants associated with coronary artery disease disease status. Instead of treating each feature independently as in conventional Bayesian variable selection methods, we propose an innovative prior for the inclusion probabilities of genetic variants that accounts for their ordering structure. We assume that neighboring variants are more likely to be selected together as they tend to be highly correlated and have similar biological functions. Additionally, we propose to group participating subjects based on underlying population structure and fit separate regressions, so that the regression coefficients can better reflect different disease risks in different population groups. Our approach borrows strength across regression models through an innovative prior inspired by the Markov random fields. The proposed framework can improve variable selection and prediction performances as demonstrated in the simulation studies. We also apply the proposed framework to the CATHeterization GENetics data with binary Coronary artery disease disease status.

Surveillance for SARS-CoV-2 and its variants in wastewater of tertiary care hospitals correlates with increasing case burden and outbreaks.

Wastewater-based SARS-CoV-2 surveillance enables unbiased and comprehensive monitoring of defined sewersheds. We performed real-time monitoring of hospital wastewater that differentiated Delta and Omicron variants within total SARS-CoV-2-RNA, enabling correlation to COVID-19 cases from three tertiary-care facilities with >2100 inpatient beds in Calgary, Canada. RNA was extracted from hospital wastewater between August/2021 and January/2022, and SARS-CoV-2 quantified using RT-qPCR. Assays targeting R203M and R203K/G204R established the proportional abundance of Delta and Omicron, respectively. Total and variant-specific SARS-CoV-2 in wastewater was compared to data for variant specific COVID-19 hospitalizations, hospital-acquired infections, and outbreaks. Ninety-six percent (188/196) of wastewater samples were SARS-CoV-2 positive. Total SARS-CoV-2 RNA levels in wastewater increased in tandem with total prevalent cases (Delta plus Omicron). Variant-specific assessments showed this increase to be mainly driven by Omicron. Hospital-acquired cases of COVID-19 were associated with large spikes in wastewater SARS-CoV-2 and levels were significantly increased during outbreaks relative to nonoutbreak periods for total SARS-CoV2, Delta and Omicron. SARS-CoV-2 in hospital wastewater was significantly higher during the Omicron-wave irrespective of outbreaks. Wastewater-based monitoring of SARS-CoV-2 and its variants represents a novel tool for passive COVID-19 infection surveillance, case identification, containment, and potentially to mitigate viral spread in hospitals.

The sibling effect on neurodevelopment of preschoolers under China's newly relaxed child policy: A national retrospective cohort study.

Introduction: The change in Chinese fertility policy brings new challenges and considerations for children's health outcomes; however, very little is known about the interaction between siblings, family socioeconomic status (SES), and neurodevelopment in the Chinese preschool-age population. Therefore, this study aimed to develop a new explanatory pathway from sibling effect to early childhood development and explored the mediation effect of family SES in the pathway. Methods: From April 2018 to December 2019, we conducted a national retrospective cohort study in 551 cities in China, and a total of 115,915 preschool-aged children were selected for the final analysis. Children's neurodevelopment, including Communication, Gross motor, Fine motor, Problem-solving, and Personal-social, was assessed with the Ages & Stages Questionnaires, Third Edition (ASQ-3). Hypothesis tests and multilevel regression models were used to assess the associations and their strength between sibling effect and neurodevelopmental delay. Pathway analysis was used to verify the mediation effect of SES. Results: The results showed that there were significant risk effects of a sibling on preschoolers' overall neurodevelopment including communication, gross motor, fine motor, and problem-solving delay. The adjustment of family SES, however, brought a reversal of this association. The results of the mediation model illustrated a direct, protective effect of one-sibling status (ßASQ-delay = -0.09; ßASQ-scores = 0.07; p < 0.001), and an indirect, risk effect from one-sibling status through family SES to neurodevelopment outcomes (ßASQ-delay =0.12; ßASQ-scores = -0.12; p < 0.001). The total sibling effect was weakened but remained negative (ßASQ-delay =0.03; ßASQ-scores = -0.05; p < 0.001). Discussion: This study concluded that family SES mediated the negative effects of one sibling on early child development. To enhance the positive influence of sibling addition, we suggested providing more resources and instructions to the families with less educated and poorer employed parents under the coming multi-child era.

Prenatal second-hand smoke exposure and the risk of suspected developmental coordination disorder in preschoolers: A nationwide retrospective cohort study in China.

Prenatal exposure to second-hand smoke (SHS) is associated with increased neurodevelopmental problems in children, however, its impact on the risk of developmental coordination disorder (DCD) in preschoolers have not been studied thoroughly. Herein, we probed this association based on a nationwide retrospective cohort study of 149,005 preschoolers in China. We divided the objects into the prenatal SHS-exposed group or the no prenatal smoke exposed group (NS-exposed group). Preschoolers were assessed for motor proficiency by the Chinese version of Little Developmental Coordination Disorder Questionnaire (LDCDQ). Multivariable logistic regression was used to evaluate the associations. The prevalence of prenatal SHS exposure was 23.89%. Generally, the prevalence of suspected DCD was significantly higher in prenatal SHS-exposed group (16.38% VS. 14.19%, P < 0.001). With the increase of age, the mean total scores of LDCDQ of both boys and girls increased gradually; and the prevalence of suspected DCD in girls was higher than that in boys in the same age group. After adjusting for covariates, prenatal SHS exposure had the negative association with the total score of LDCDQ and increased the risk of suspected DCD. Our results suggest a need for interventions designed to reduce maternal SHS exposure during pregnancy, early screen for DCD and increase targeted movement and coordination skill training for vulnerable children.

Discussion of tumor mutation burden as an indicator to predict efficacy of immune checkpoint inhibitors: A case report.

There are many treatment options for advanced lung cancer, among which immunotherapy has developed rapidly and benefited a lot of patients. However, immunotherapy can only benefit a subgroup of patients, and how to select patients suitable for this therapy is critical. Tumor mutation burden (TMB) is one of the important reference indicators for immune checkpoint inhibitors (ICIs). However, there are many factors influencing the usage of this indicator, which will lead to considerable consequences if not treated well. In this study, we performed a case study on a male advanced lung squamous cell carcinoma patient of age 83. The patient suffered from "cough and sputum", and did chest CT scans on 24 October 2018, which showed "a mass-like mass in the anterior segment of the right lung upper lobe, about 38mm×28mm". He was treated with systemic chemotherapy; however, the tumor was still under progression. Although PD-L1 was not tested in gene testing, he had a TMB value of 10.26 mutations/Mb with a quantile value 88.63%. Thus, "toripalimab injection" was added as immunotherapy and the size of the lesion decreased. In summary, we adopted a clinical case as the basis to explore the value and significance of TMB in immunotherapy in this study. We hope that more predictive molecular markers will be discovered, which will bring more treatment methods for advanced lung cancer.

Statistical framework to support the epidemiological interpretation of SARS-CoV-2 concentration in municipal wastewater.

The ribonucleic acid (RNA) of the severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) is detectable in municipal wastewater as infected individuals can shed the virus in their feces. Viral concentration in wastewater can inform the severity of the COVID-19 pandemic but observations can be noisy and sparse and hence hamper the epidemiological interpretation. Motivated by a Canadian nationwide wastewater surveillance data set, unlike previous studies, we propose a novel Bayesian statistical framework based on the theories of functional data analysis to tackle the challenges embedded in the longitudinal wastewater monitoring data. By employing this framework to analyze the large-scale data set from the nationwide wastewater surveillance program covering 15 sampling sites across Canada, we successfully detect the true trends of viral concentration out of noisy and sparsely observed viral concentrations, and accurately forecast the future trajectory of viral concentrations in wastewater. Along with the excellent performance assessment using simulated data, this study shows that the proposed novel framework is a useful statistical tool and has a significant potential in supporting the epidemiological interpretation of noisy viral concentration measurements from wastewater samples in a real-life setting.

Longitudinal SARS-CoV-2 RNA wastewater monitoring across a range of scales correlates with total and regional COVID-19 burden in a well-defined urban population.

Wastewater-based epidemiology (WBE) is an emerging surveillance tool that has been used to monitor the ongoing COVID-19 pandemic by tracking SARS-CoV-2 RNA shed into wastewater. WBE was performed to monitor the occurrence and spread of SARS-CoV-2 from three wastewater treatment plants (WWTP) and six neighborhoods in the city of Calgary, Canada (population 1.44 million). A total of 222 WWTP and 192 neighborhood samples were collected from June 2020 to May 2021, encompassing the end of the first-wave (June 2020), the second-wave (November end to December 2020) and the third-wave of the COVID-19 pandemic (mid-April to May 2021). Flow-weighted 24-hour composite samples were processed to extract RNA that was then analyzed for two SARS-CoV-2-specific regions of the nucleocapsid gene, N1 and N2, using reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Using this approach SARS-CoV-2 RNA was detected in 98.06% (406/414) of wastewater samples. SARS-CoV-2 RNA abundance was compared to clinically diagnosed COVID-19 cases organized by the three-digit postal code of affected individuals' primary residences, enabling correlation analysis at neighborhood, WWTP and city-wide scales. Strong correlations were observed between N1 & N2 gene signals in wastewater and new daily cases for WWTPs and neighborhoods. Similarly, when flow rates at Calgary's three WWTPs were used to normalize observed concentrations of SARS-CoV-2 RNA and combine them into a city-wide signal, this was strongly correlated with regionally diagnosed COVID-19 cases and clinical test percent positivity rate. Linked census data demonstrated disproportionate SARS-CoV-2 in wastewater from areas of the city with lower socioeconomic status and more racialized communities. WBE across a range of urban scales was demonstrated to be an effective mechanism of COVID-19 surveillance.

Network Pharmacology and Molecular Docking Study of the Chinese Miao Medicine Sidaxue in the Treatment of Rheumatoid Arthritis.

PURPOSE: This study aimed to investigate the molecular mechanisms of Compound Sidaxue (SX), a prescription of Chinese Miao medicine, in treating rheumatoid arthritis (RA) using network pharmacology and in vivo experimental approaches. METHODS: Network pharmacology was adopted to detect the active components of four Traditional Chinese herbal medicine (TCM) of SX, and the key targets and signaling pathways in the treatment of RA were predicted, and the key components and targets were screened for molecular docking. The predicted targets and pathways were validated in bovine type II collagen and incomplete Freund's adjuvant emulsifier-induced rat RA model. RESULTS: In this study, we identified 33 active components from SX, predicted to act on 44 RA-associated targets by network pharmacology. PPI network demonstrated that TNF-α, VEGF-A, IL-2, IL-6, AKT, PI3K, STAT1 may serve as the key targets of SX for the treatment of RA. The main functional pathways involving these key targets include PI3K-AKT signaling pathway, TNF signaling pathway, NF-κB signaling pathway. Molecular docking analysis found that the active components ß-amyrin, cajanin, eleutheroside A have high affinity for TNF-α, VEGFA, IL-2, AKT, and PI3K, etc. SX can improve joint swelling in Collagen-induced arthritis (CIA) rats, reduce inflammatory cell infiltration and angiogenesis in joint synovial tissue, and down-regulate IL-2, IL-6, TNF-α, VEGF, PI3K, AKT, p-AKT, NF-κBp65, the expression of p-NF-κBp65, STAT1, and PTGS2 are used to control the exacerbation of inflammation and alleviate the proliferation of synovial pannus, and at the same time play the role of cartilage protection to achieve the effect of treating RA. CONCLUSION: Through a network pharmacology approach and animal study, we predicted and validated the active compounds of SX and their potential targets for RA treatment. The results suggest that SX can markedly alleviate CIA rat by modulating the VEGF/PI3K/AKT signaling pathway, TNF-α signaling pathway, IL/NF-κB signaling pathway.

Association of Gestational Age at Birth With Subsequent Suspected Developmental Coordination Disorder in Early Childhood in China.

Importance: It remains unknown whether children born at different degrees of prematurity, early term, and post term might have a higher risk of developmental coordination disorder (DCD) compared with completely full-term children (39-40 gestational weeks). Objective: To differentiate between suspected DCD in children with different gestational ages based on a national representative sample in China. Design, Setting, and Participants: A retrospective cohort study was conducted in China from April 1, 2018, to December 31, 2019. A total of 152 433 children aged 3 to 5 years from 2403 public kindergartens in 551 cities of China were included in the final analysis. A multilevel regression model was developed to determine the strength of association for different gestational ages associated with suspected DCD when considering kindergartens as clusters. Main Outcomes and Measures: Children's motor performance was assessed using the Little Developmental Coordination Disorder Questionnaire, completed by their parents. Gestational age was determined according to the mother's medical records and divided into 7 categories: completely full term (39 to 40 weeks' gestation), very preterm (<32 weeks), moderately preterm (32-33 weeks), late preterm (34-36 weeks), early term (37-38 weeks), late term (41 weeks), and post term (>41 weeks). Results: A total of 152 433 children aged 3 to 5 years (mean [SD] age, 4.5 [0.8] years), including 80 370 boys (52.7%) and 72 063 girls (47.3%), were included in the study. There were 45 052 children (29.6%) aged 3 years, 59 796 (39.2%) aged 4 years, and 47 585 (31.2%) aged 5 years. Children who were born very preterm (odds ratio [OR], 1.35; 95% CI, 1.23-1.48), moderately preterm (OR, 1.18; 95% CI, 1.02-1.36), late preterm (OR, 1.24; 95% CI, 1.16-1.32), early term (OR, 1.11; 95% CI, 1.06-1.16), and post term (OR, 1.17; 95% CI, 1.07-1.27) were more likely to be classified in the suspected DCD category on the Little Developmental Coordination Disorder Questionnaire than completely full-term children after adjusting for the same characteristics. Additionally, there was no association with suspected DCD in younger (aged 3 years) early-term and postterm children by stratified analyses. Conclusions and Relevance: In this cohort study, every degree of prematurity at birth, early-term birth, and postterm birth were associated with suspected DCD when compared with full-term birth. These findings have important implications for understanding motor development in children born at different gestational ages. Long-term follow-up and rehabilitation interventions should be considered for children born early and post term.

Functional random forests for curve response.

The rapid advancement of functional data in various application fields has increased the demand for advanced statistical approaches that can incorporate complex structures and nonlinear associations. In this article, we propose a novel functional random forests (FunFor) approach to model the functional data response that is densely and regularly measured, as an extension of the landmark work of Breiman, who introduced traditional random forests for a univariate response. The FunFor approach is able to predict curve responses for new observations and selects important variables from a large set of scalar predictors. The FunFor approach inherits the efficiency of the traditional random forest approach in detecting complex relationships, including nonlinear and high-order interactions. Additionally, it is a non-parametric approach without the imposition of parametric and distributional assumptions. Eight simulation settings and one real-data analysis consistently demonstrate the excellent performance of the FunFor approach in various scenarios. In particular, FunFor successfully ranks the true predictors as the most important variables, while achieving the most robust variable sections and the smallest prediction errors when comparing it with three other relevant approaches. Although motivated by a biological leaf shape data analysis, the proposed FunFor approach has great potential to be widely applied in various fields due to its minimal requirement on tuning parameters and its distribution-free and model-free nature. An R package named 'FunFor', implementing the FunFor approach, is available at GitHub.

Network Pharmacology Analysis of the Mechanisms of Compound Herba Sarcandrae (Fufang Zhongjiefeng) Aerosol in Chronic Pharyngitis Treatment.

PURPOSE: This study aimed to investigate the molecular mechanisms of compound herba Sarcandrae aerosol, also known as the Fufang Zhongjiefeng (FFZJF) aerosol, in treating chronic pharyngitis (CP) using network pharmacology and in vivo experimental approaches. METHODS: Active compounds and putative targets of five herbs in FFZJF were identified from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform, Chemistry Database, and Swiss Target Prediction databases. The therapeutic targets of CP were obtained from OMIM, Durgbank, DisGeNT, and GAD databases. The active compounds-target networks were constructed using Cytoscape 3.6.1. The overlapping targets of FFZJF active compounds and CP targets were further analyzed using the String database to construct protein-protein interaction (PPI) network. KEGG pathway and Gene Ontology enrichment analysis was performed using the Database for Annotation, Visualization, and Integrated Discovery. The predicted targets and pathways were validated in a group A ß-hemolytic streptococcus-induced rat CP model. RESULTS: There were 45 active compounds identified from FFZJF and 11 potential protein targets identified for CP treatment. PPI network demonstrated that IL6, PTGS2, TLR-4, and TNF may serve as the key targets of FFZJF for the treatment of CP. The main functional pathways involving these key targets include cytokine secretion, inflammatory response, MyD88-dependent toll-like receptor signaling pathway, toll-like receptor signaling pathway, TNF signaling pathway, and NF-κB signaling pathway. In a rat CP model, the elevation of serum TNF-α, IL1ß, and IL6 levels, as well as the upregulation of TLR-4, MyD88, NF-κB P65 in the pharyngeal mucosal tissues could be effectively reduced by FFZJF treatment in a dose-dependent manner. CONCLUSION: Through a network pharmacology approach and animal study, we predicted and validated the active compounds of FFZJF and their potential targets for CP treatment. The results suggest that FFZJF can markedly alleviate GAS-induced chronic pharyngitis by modulating the TLR-4/MyD88/NF-κB signaling pathways.

Statistical Learning Methods Applicable to Genome-Wide Association Studies on Unbalanced Case-Control Disease Data.

Despite the fact that imbalance between case and control groups is prevalent in genome-wide association studies (GWAS), it is often overlooked. This imbalance is getting more significant and urgent as the rapid growth of biobanks and electronic health records have enabled the collection of thousands of phenotypes from large cohorts, in particular for diseases with low prevalence. The unbalanced binary traits pose serious challenges to traditional statistical methods in terms of both genomic selection and disease prediction. For example, the well-established linear mixed models (LMM) yield inflated type I error rates in the presence of unbalanced case-control ratios. In this article, we review multiple statistical approaches that have been developed to overcome the inaccuracy caused by the unbalanced case-control ratio, with the advantages and limitations of each approach commented. In addition, we also explore the potential for applying several powerful and popular state-of-the-art machine-learning approaches, which have not been applied to the GWAS field yet. This review paves the way for better analysis and understanding of the unbalanced case-control disease data in GWAS.

Concentration and integrity indexes of urine cell-free DNA as promising biomarkers for early lung cancer diagnosis.

Aim: To explore the role of urine cell-free DNA (ucfDNA) concentration and integrity indexes as potential biomarkers for lung cancer diagnosis. Materials & methods: Quantitative real-time PCR targeting Arthrobacter luteus (ALU) repeats at three size fragments (ALU-60, 115 and 247 bp) was performed in 55 lung cancer patients and 35 healthy individuals. Results: ucfDNA concentration and integrity indexes were significantly higher in lung cancer patients than in healthy controls. The area under the receiver operating characteristic curve for differentiating patients with stage I/II from healthy controls by ALU fragments concentration were 0.856, 0.909 and 0.932, respectively. In addition, the ucfDNA integrity indexes in patients with lymph node metastasis were significantly higher than in patients with non-metastatic. Conclusion: ucfDNA concentration and integrity indexes could serve as promising biomarkers for lung cancer diagnosis.

[Neurobehavioral development of 25 254 children with different gestational ages at birth in three cities of China].

OBJECTIVE: To study the effect of gestational age at birth on the neurobehavioral development of preschool children. METHODS: A total of 25 254 preschool children from Ma'anshan of Anhui Province, Taizhou of Zhejiang Province, and Yangzhou of Jiangsu Province were enrolled. The preschool children were divided into three groups based on their gestational ages at birth: preterm group (2 760 cases; 28-36+6 weeks), early term group (6 005 cases; 37-38+6 weeks), and full term group (16 489 cases; ≥39 weeks). The Ages and Stages Questionnaires-Third Edition (ASQ-3) was employed to evaluate the children's neurobehavioral development. RESULTS: The preterm group had significantly lower scores of the five domains of ASQ-3, communication, gross motor, fine motor, problem solving, and personal-social, than the full term group (P<0.05), and significantly lower scores of communication, gross motor, fine motor, and problem solving than the early term group (P<0.05). There were no significant differences in the scores of the five domains of ASQ-3 between the early term and full term groups (P>0.05). The multiple linear regression analysis indicated a significant positive correlation between gestational age and the five domains of ASQ-3 after adjustment for confounding factors including sex, age, body mass index, and parental education level (P<0.01). CONCLUSIONS: Children born preterm have poorer neurobehavioral development than those born full term and early term, whereas children born full term and early term have similar neurobehavioral development. Gestational age at birth is an independent influencing factor for neurobehavioral development in preschool children.