HEARTSVG: a fast and accurate method for identifying spatially variable genes in large-scale spatial transcriptomics.

Identifying spatially variable genes (SVGs) is crucial for understanding the spatiotemporal characteristics of diseases and tissue structures, posing a distinctive challenge in spatial transcriptomics research. We propose HEARTSVG, a distribution-free, test-based method for fast and accurately identifying spatially variable genes in large-scale spatial transcriptomic data. Extensive simulations demonstrate that HEARTSVG outperforms state-of-the-art methods with higher F 1 scores (average F 1 Score=0.948), improved computational efficiency, scalability, and reduced false positives (FPs). Through analysis of twelve real datasets from various spatial transcriptomic technologies, HEARTSVG identifies a greater number of biologically significant SVGs (average AUC = 0.792) than other comparative methods without prespecifying spatial patterns. Furthermore, by clustering SVGs, we uncover two distinct tumor spatial domains characterized by unique spatial expression patterns, spatial-temporal locations, and biological functions in human colorectal cancer data, unraveling the complexity of tumors.

Issues and Solutions in Psychiatric Clinical Trial with Case Studies.

The coronavirus disease-2019 pandemic resulted in a major increase in depression and anxiety disorders worldwide, which increased the demand for mental health services. However, clinical interventions for treating mental disorders are currently insufficient to meet this growing demand. There is an urgent need to conduct scientific and standardized clinical research that are consistent with the features of mental disorders in order to deliver more effective and safer therapies in the clinic. Our study aimed to expose the challenges, complexities of study design, ethical issues, sample selection, and efficacy evaluation in clinical research for mental disorders. The reliance on subjective symptom presentation and rating scales for diagnosing mental diseases was discovered, emphasizing the lack of clear biological standards, which hampers the construction of rigorous research criteria. We underlined the possibility of psychotherapy in efficacy evaluation alongside medication treatment, proposing for a multidisciplinary approach comprising psychiatrists, neuroscientists, and statisticians. To comprehend mental disorders progression, we recommend the development of artificial intelligence integrated evaluation tools, the use of precise biomarkers, and the strengthening of longitudinal designs. In addition, we advocate for international collaboration to diversity samples and increase the dependability of findings, with the goal of improving clinical research quality in mental disorders through sample representativeness, accurate medical history gathering, and adherence to ethical principles.

The association between air pollutant exposure and cerebral small vessel disease imaging markers with modifying effects of PRS-defined genetic susceptibility.

Studies have highlighted a possible link between air pollution and cerebral small vessel disease (CSVD) imaging markers. However, the exact association and effects of polygenic risk score (PRS) defined genetic susceptibility remains unclear. This cross-sectional study used data from the UK Biobank. Participants aged 40-69 years were recruited between the year 2006 and 2010. The annual average concentrations of NOX, NO2, PM2.5, PM2.5-10, PM2.5 absorbance, and PM10, were estimated, and joint exposure to multiple air pollutants was reflected in the air pollution index (APEX). Air pollutant exposure was classified into the low (T1), intermediate (T2), and high (T3) tertiles. Three CSVD markers were used: white matter hyper-intensity (WMH), mean diffusivity (MD), and fractional anisotropy (FA). The first principal components of the MD and FA measures in the 48 white matter tracts were analysed. The sample consisted of 44,470 participants from the UK Biobank. The median (T1-T3) concentrations of pollutants were as follows: NO2, 25.5 (22.4-28.7) µg/m3; NOx, 41.3 (36.2-46.7) µg/m3; PM10, 15.9 (15.4-16.4) µg/m3; PM2.5, 9.9 (9.5-10.3) µg/m3; PM2.5 absorbance, 1.1 (1.0-1.2) per metre; and PM2.5-10, 6.1 (5.9-6.3) µg/m3. Compared with the low group, the high group's APEX, NOX, and PM2.5 levels were associated with increased WMH volumes, and the estimates (95 %CI) were 0.024 (0.003, 0.044), 0.030 (0.010, 0.050), and 0.032 (0.011, 0.053), respectively, after adjusting for potential confounders. APEX, PM10, PM2.5 absorbance, and PM2.5-10 exposure in the high group were associated with increased FA values compared to that in the low group. Sex-specific analyses revealed associations only in females. Regarding the combined associations of air pollutant exposure and PRS-defined genetic susceptibility with CSVD markers, the associations of NO2, NOX, PM2.5, and PM2.5-10 with WMH were more profound in females with low PRS-defined genetic susceptibility, and the associations of PM10, PM2.5, and PM2.5 absorbance with FA were more profound in females with higher PRS-defined genetic susceptibility. Our study demonstrated that air pollutant exposure may be associated with CSVD imaging markers, with females being more susceptible, and that PRS-defined genetic susceptibility may modify the associations of air pollutants.

Deep Learning-Based Detect-Then-Track Pipeline for Treatment Outcome Assessments in Immunotherapy-Treated Liver Cancer.

Accurate treatment outcome assessment is crucial in clinical trials. However, due to the image-reading subjectivity, there exist discrepancies among different radiologists. The situation is common in liver cancer due to the complexity of abdominal scans and the heterogeneity of radiological imaging manifestations in liver subtypes. Therefore, we developed a deep learning-based detect-then-track pipeline that can automatically identify liver lesions from 3D CT scans then longitudinally track target lesions, thereby providing the evaluation of RECIST treatment outcomes in liver cancer. We constructed and validated the pipeline on 173 multi-national patients (344 venous-phase CT scans) consisting of a public dataset and two in-house cohorts of 28 centers. The proposed pipeline achieved a mean average precision of 0.806 and 0.726 of lesion detection on the validation and test sets. The model's diameter measurement reliability and consistency are significantly higher than that of clinicians (p = 1.6 × 10-4). The pipeline can make precise lesion tracking with accuracies of 85.7% and 90.8% then finally yield the RECIST accuracies of 82.1% and 81.4% on the validation and test sets. Our proposed pipeline can provide precise and convenient RECIST outcome assessments and has the potential to aid clinicians with more efficient therapeutic decisions.

Harnessing TME depicted by histological images to improve cancer prognosis through a deep learning system.

Spatial transcriptomics (ST) provides insights into the tumor microenvironment (TME), which is closely associated with cancer prognosis, but ST has limited clinical availability. In this study, we provide a powerful deep learning system to augment TME information based on histological images for patients without ST data, thereby empowering precise cancer prognosis. The system provides two connections to bridge existing gaps. The first is the integrated graph and image deep learning (IGI-DL) model, which predicts ST expression based on histological images with a 0.171 increase in mean correlation across three cancer types compared with five existing methods. The second connection is the cancer prognosis prediction model, based on TME depicted by spatial gene expression. Our survival model, using graphs with predicted ST features, achieves superior accuracy with a concordance index of 0.747 and 0.725 for The Cancer Genome Atlas breast cancer and colorectal cancer cohorts, outperforming other survival models. For the external Molecular and Cellular Oncology colorectal cancer cohort, our survival model maintains a stable advantage.

Predicting long-term progression of Alzheimer's disease using a multimodal deep learning model incorporating interaction effects.

BACKGROUND: Identifying individuals with mild cognitive impairment (MCI) at risk of progressing to Alzheimer's disease (AD) provides a unique opportunity for early interventions. Therefore, accurate and long-term prediction of the conversion from MCI to AD is desired but, to date, remains challenging. Here, we developed an interpretable deep learning model featuring a novel design that incorporates interaction effects and multimodality to improve the prediction accuracy and horizon for MCI-to-AD progression. METHODS: This multi-center, multi-cohort retrospective study collected structural magnetic resonance imaging (sMRI), clinical assessments, and genetic polymorphism data of 252 patients with MCI at baseline from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Our deep learning model was cross-validated on the ADNI-1 and ADNI-2/GO cohorts and further generalized in the ongoing ADNI-3 cohort. We evaluated the model performance using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and F1 score. RESULTS: On the cross-validation set, our model achieved superior results for predicting MCI conversion within 4 years (AUC, 0.962; accuracy, 92.92%; sensitivity, 88.89%; specificity, 95.33%) compared to all existing studies. In the independent test, our model exhibited consistent performance with an AUC of 0.939 and an accuracy of 92.86%. Integrating interaction effects and multimodal data into the model significantly increased prediction accuracy by 4.76% (P = 0.01) and 4.29% (P = 0.03), respectively. Furthermore, our model demonstrated robustness to inter-center and inter-scanner variability, while generating interpretable predictions by quantifying the contribution of multimodal biomarkers. CONCLUSIONS: The proposed deep learning model presents a novel perspective by combining interaction effects and multimodality, leading to more accurate and longer-term predictions of AD progression, which promises to improve pre-dementia patient care.

Single-cell biclustering for cell-specific transcriptomic perturbation detection in AD progression.

The pathogenesis of Alzheimer disease (AD) involves complex gene regulatory changes across different cell types. To help decipher this complexity, we introduce single-cell Bayesian biclustering (scBC), a framework for identifying cell-specific gene network biomarkers in scRNA and snRNA-seq data. Through biclustering, scBC enables the analysis of perturbations in functional gene modules at the single-cell level. Applying the scBC framework to AD snRNA-seq data reveals the perturbations within gene modules across distinct cell groups and sheds light on gene-cell correlations during AD progression. Notably, our method helps to overcome common challenges in single-cell data analysis, including batch effects and dropout events. Incorporating prior knowledge further enables the framework to yield more biologically interpretable results. Comparative analyses on simulated and real-world datasets demonstrate the precision and robustness of our approach compared to other state-of-the-art biclustering methods. scBC holds potential for unraveling the mechanisms underlying polygenic diseases characterized by intricate gene coexpression patterns.

Bayesian network-based Mendelian randomization for variant prioritization and phenotypic causal inference.

Mendelian randomization is a powerful method for inferring causal relationships. However, obtaining suitable genetic instrumental variables is often challenging due to gene interaction, linkage, and pleiotropy. We propose Bayesian network-based Mendelian randomization (BNMR), a Bayesian causal learning and inference framework using individual-level data. BNMR employs the random graph forest, an ensemble Bayesian network structural learning process, to prioritize candidate genetic variants and select appropriate instrumental variables, and then obtains a pleiotropy-robust estimate by incorporating a shrinkage prior in the Bayesian framework. Simulations demonstrate BNMR can efficiently reduce the false-positive discoveries in variant selection, and outperforms existing MR methods in terms of accuracy and statistical power in effect estimation. With application to the UK Biobank, BNMR exhibits its capacity in handling modern genomic data, and reveals the causal relationships from hematological traits to blood pressures and psychiatric disorders. Its effectiveness in handling complex genetic structures and modern genomic data highlights the potential to facilitate real-world evidence studies, making it a promising tool for advancing our understanding of causal mechanisms.

A generalized calibrated Bayesian hierarchical modeling approach to basket trials with multiple endpoints.

A basket trial simultaneously evaluates a treatment in multiple cancer subtypes, offering an effective way to accelerate drug development in multiple indications. Many basket trials are designed and monitored based on a single efficacy endpoint, primarily the tumor response. For molecular targeted or immunotherapy agents, however, a single efficacy endpoint cannot adequately characterize the treatment effect. It is increasingly important to use more complex endpoints to comprehensively assess the risk-benefit profile of such targeted therapies. We extend the calibrated Bayesian hierarchical modeling approach to monitor phase II basket trials with multiple endpoints. We propose two generalizations, one based on the latent variable approach and the other based on the multinomial-normal hierarchical model, to accommodate different types of endpoints and dependence assumptions regarding information sharing. We introduce shrinkage parameters as functions of statistics measuring homogeneity among subgroups and propose a general calibration approach to determine the functional forms. Theoretical properties of the generalized hierarchical models are investigated. Simulation studies demonstrate that the monitoring procedure based on the generalized approach yields desirable operating characteristics.

Types of On-Screen Content and Mental Health in Kindergarten Children.

Importance: Excessive screen time has been associated with a higher risk for mental health problems, but whether the associations differ by screen content types is unclear. Objective: To examine the allocation of and longitudinal changes in screen exposure across different content types and to explore their associations with mental health in children aged 3 to 6 years. Design, Setting, and Participants: This cohort study used 3-wave, lagged generalized estimating equation models to analyze data from the Shanghai Children's Health, Education and Lifestyle Evaluation-Preschool (SCHEDULE-P) study in Shanghai, China. The cohort was a representative sample of kindergarten children. Data were collected between November 2016 and May 2019 when children were aged 3 to 4 years (wave 1), 4 to 5 years (wave 2), and 5 to 6 years (wave 3). Data analysis was performed between June 2022 and May 2023. Exposure: Screen exposure (total daily time and time with each type of content, including educational programs, entertainment programs, non-child-directed programs, electronic games, and social media) was collected when children were aged 3, 5, and 6 years. Main Outcomes and Measures: Mental health of children at age 3, 5, and 6 years was reported by parents using the Strengths and Difficulties Questionnaire. Results: Of the 15 965 children included in the representative sample, 8270 were males (51.7%) and the mean (SD) age at wave 1 was 3.73 (0.30) years. As children developed from ages 3 to 6 years, the proportion of screen exposure to educational programs (≤1 hour per day: 45.0% [95% CI, 43.5%-46.5%] to 26.8% [95% CI, 25.3%-28.3%]) and entertainment programs (≤1 hour per day: 44.4% [95% CI, 42.8%-45.9%] to 32.1% [95% CI, 30.4%-33.9%]) decreased, whereas exposure to social media increased (≤1 hour per day: 1.5% [95% CI, 1.2%-1.9%] to 27.1% [95% CI, 25.5%-28.7%]). The associations between on-screen content and mental health varied. For a given total screen time, a higher proportion of screen exposure to educational programs was associated with a lower risk for mental health problems (adjusted odds ratio [AOR], 0.73; 95% CI, 0.60-0.90), whereas non-child-directed programs were associated with a higher risk for such problems (AOR, 2.82; 95% CI, 1.91-4.18). Regardless of the content, total screen time was consistently associated with mental health problems. Conclusions and relevance: Results of this study indicated that both total screen time and different types of content were associated with mental health problems in children aged 3 to 6 years. Limiting children's screen time, prioritizing educational programs, and avoiding non-child-directed programs are recommended.

Prediction of outpatient waiting time: using machine learning in a tertiary children's hospital.

Background: Accurately predicting waiting time for patients is crucial for effective hospital management. The present study examined the prediction of outpatient waiting time in a Chinese pediatric hospital through the use of machine learning algorithms. If patients are informed about their waiting time in advance, they can make more informed decisions and better plan their visit on the day of admission. Methods: First, a novel classification method for the outpatient clinic in the Chinese pediatric hospital was proposed, which was based on medical knowledge and statistical analysis. Subsequently, four machine learning algorithms [linear regression (LR), random forest (RF), gradient boosting decision tree (GBDT), and K-nearest neighbor (KNN)] were used to construct prediction models of the waiting time of patients in four department categories. Results: The three machine learning algorithms outperformed LR in the four department categories. The optimal model for Internal Medicine Department I was the RF model, with a mean absolute error (MAE) of 5.03 minutes, which was 47.60% lower than that of the LR model. The optimal model for the other three categories was the GBDT model. The MAE of the GBDT model was decreased by 28.26%, 35.86%, and 33.10%, respectively compared to that of the LR model. Conclusions: Machine learning can predict the outpatient waiting time of pediatric hospitals well and ease patient anxiety when waiting in line without medical appointments. This study offers key insights into enhancing healthcare services and reaffirms the dedication of Chinese pediatric hospitals to providing efficient and patient-centric care.

A novel cost function for nuclei segmentation and classification in imbalanced histopathology data-sets.

Cancer is a major global health problem, causing millions of deaths yearly. Histopathological analysis plays a crucial role in detecting and diagnosing various types of cancer, enabling an accurate diagnosis to inform targeted treatment planning, allowing for better cancer staging, and ultimately improving prognosis. We aim to detect cancer earlier, which can ultimately help reduce mortality rates and enhance patients' quality of life. However, detecting and classifying rare cells is a key challenge for pathologists and researchers. Many histopathological data-sets contain imbalanced data, with only a few instances of rare cells whose unique morphological structures can impede early diagnosis efforts. Our model, SPNet, a spatially aware convolutional neural network, addresses this problem by employing a spatial data balancing technique, enhancing the classification of rare nuclei by 21.8 %. Since nuclei often cluster and exhibit patterns of the same class, SPNet's novel cost function targets spatial regions, resulting in a 1.9 % increase in the F1 classification of rare class types within the CoNSeP dataset. When integrated with a ResNet50-SE encoder, SPNet increases the mean F1 score for classifying all nuclei in the CoNSeP dataset by 4.3 %, compared to the benchmark set by the state-of-the-art HoVer-Net model. The potential integration of SPNet into existing medical devices could allow us to streamline diagnostic processes and minimise false negatives.

Individualized red-cell transfusion strategy for non-cardiac surgery in adults: a randomized controlled trial.

BACKGROUND: Red-cell transfusion is critical for surgery during the peri-operative period; however, the transfusion threshold remains controversial mainly owing to the diversity among patients. The patient's medical status should be evaluated before making a transfusion decision. Herein, we developed an individualized transfusion strategy using the West-China-Liu's Score based on the physiology of oxygen delivery/consumption balance and designed an open-label, multicenter, randomized clinical trial to verify whether it reduced red cell requirement as compared with that associated with restrictive and liberal strategies safely and effectively, providing valid evidence for peri-operative transfusion. METHODS: Patients aged >14 years undergoing elective non-cardiac surgery with estimated blood loss > 1000 mL or 20% blood volume and hemoglobin concentration <10 g/dL were randomly assigned to an individualized strategy, a restrictive strategy following China's guideline or a liberal strategy with a transfusion threshold of hemoglobin concentration <9.5 g/dL. We evaluated two primary outcomes: the proportion of patients who received red blood cells (superiority test) and a composite of in-hospital complications and all-cause mortality by day 30 (non-inferiority test). RESULTS: We enrolled 1182 patients: 379, 419, and 384 received individualized, restrictive, and liberal strategies, respectively. Approximately 30.6% (116/379) of patients in the individualized strategy received a red-cell transfusion, less than 62.5% (262/419) in the restrictive strategy (absolute risk difference, 31.92%; 97.5% confidence interval [CI]: 24.42-39.42%; odds ratio, 3.78%; 97.5% CI: 2.70-5.30%; P <0.001), and 89.8% (345/384) in the liberal strategy (absolute risk difference, 59.24%; 97.5% CI: 52.91-65.57%; odds ratio, 20.06; 97.5% CI: 12.74-31.57; P <0.001). No statistically significant differences were found in the composite of in-hospital complications and mortality by day 30 among the three strategies. CONCLUSION: The individualized red-cell transfusion strategy using the West-China-Liu's Score reduced red-cell transfusion without increasing in-hospital complications and mortality by day 30 when compared with restrictive and liberal strategies in elective non-cardiac surgeries. TRIAL REGISTRATION: ClinicalTrials.gov, NCT01597232.

In utero exposure to perfluoroalkyl substances and early childhood BMI trajectories: A mediation analysis with neonatal metabolic profiles.

BACKGROUND: In utero perfluoroalkyl substances (PFAS) exposure has been associated with childhood adiposity, but the mechanisms are poorly known. OBJECTIVE: To investigate the potential mediating role of neonatal metabolites in the relationship between prenatal PFAS exposure and childhood adiposity trajectories in the first four years of life. METHODS: We analyzed the data for 1671 mother-child pairs from the Shanghai Birth Cohort study. We included those with PFAS exposure information in early pregnancy, neonatal metabolites data and at least three child anthropometric measurements at 6, 12, 24 and/or 48 months. Body mass index (BMI) z-score trajectories were identified using latent class growth mixture modeling. The associations between PFAS concentrations and trajectory classes were assessed using multinomial logistic regression. Screening and penalization-based selection was used to identify neonatal amino acids and acylcarnitines with significant mediation effects. RESULTS: Three BMI z-score trajectories in early childhood were identified: a persistent increase trajectory (Class 1, 2.2 %), a stable trajectory (Class 2, 66 %), and a transient increase trajectory (Class 3, 32 %). Increased odds of being in Class 1 were observed in association with one log-unit increase in concentrations of perfluorooctane sulfonate (odds ratio [OR], 1.76 [95 % CI, 0.96-3.23], Class 2 as reference; OR, 2.36 [95 % CI, 1.27-4.40], Class 3 as reference), perfluorononanoic acid (OR, 1.90 [95 % CI, 0.97-3.72], Class 2 as reference; OR, 2.23 [95 % CI, 1.12-4.42], Class 3 as reference) and perfluorodecanoic acid (OR, 1.95 [95 % CI, 1.12-3.38], Class 2 as reference; OR, 2.14 [95 % CI, 1.22-3.76], Class 3 as reference). The effect of prenatal PFAS exposure on being in Class 1 was significantly but partly mediated by octanoylcarnitine (2.64 % for perfluorononanoic acid and 3.70 % for sum of 10 PFAS). CONCLUSIONS: In utero PFAS exposure is a risk factor for persistent growth in BMI z-score in early childhood. The alteration of neonatal acylcarnitines suggests a potential molecular pathway.

Survival prediction of stomach cancer using expression data and deep learning models with histopathological images.

Accurately predicting patient survival is essential for cancer treatment decision. However, the prognostic prediction model based on histopathological images of stomach cancer patients is still yet to be developed. We propose a deep learning-based model (MultiDeepCox-SC) that predicts overall survival in patients with stomach cancer by integrating histopathological images, clinical data, and gene expression data. The MultiDeepCox-SC not only automatedly selects patches with more information for survival prediction, without manual labeling for histopathological images, but also identifies genetic and clinical risk factors associated with survival in stomach cancer. The prognostic accuracy of the MultiDeepCox-SC (C-index = 0.744) surpasses the result only based on histopathological image (C-index = 0.660). The risk score of our model was still an independent predictor of survival outcome after adjustment for potential confounders, including pathologic stage, grade, age, race, and gender on The Cancer Genome Atlas dataset (hazard ratio 1.555, p = 3.53e-08) and the external test set (hazard ratio 2.912, p = 9.42e-4). Our fully automated online prognostic tool based on histopathological images, clinical data, and gene expression data could be utilized to improve pathologists' efficiency and accuracy (https://yu.life.sjtu.edu.cn/DeepCoxSC).

A high-efficiency differential expression method for cancer heterogeneity using large-scale single-cell RNA-sequencing data.

Colorectal cancer is a highly heterogeneous disease. Tumor heterogeneity limits the efficacy of cancer treatment. Single-cell RNA-sequencing technology (scRNA-seq) is a powerful tool for studying cancer heterogeneity at cellular resolution. The sparsity, heterogeneous diversity, and fast-growing scale of scRNA-seq data pose challenges to the flexibility, accuracy, and computing efficiency of the differential expression (DE) methods. We proposed HEART (high-efficiency and robust test), a statistical combination test that can detect DE genes with various sources of differences beyond mean expression changes. To validate the performance of HEART, we compared HEART and the other six popular DE methods on various simulation datasets with different settings by two simulation data generation mechanisms. HEART had high accuracy ( F 1 score >0.75) and brilliant computational efficiency (less than 2 min) on multiple simulation datasets in various experimental settings. HEART performed well on DE genes detection for the PBMC68K dataset quantified by UMI counts and the human brain single-cell dataset quantified by read counts ( F 1 score = 0.79, 0.65). By applying HEART to the single-cell dataset of a colorectal cancer patient, we found several potential blood-based biomarkers (CTTN, S100A4, S100A6, UBA52, FAU, and VIM) associated with colorectal cancer metastasis and validated them on additional spatial transcriptomic data of other colorectal cancer patients.

STS-BN: An efficient Bayesian network method for detecting causal SNPs.

Background: The identification of the causal SNPs of complex diseases in large-scale genome-wide association analysis is beneficial to the studies of pathogenesis, prevention, diagnosis and treatment of these diseases. However, existing applicable methods for large-scale data suffer from low accuracy. Developing powerful and accurate methods for detecting SNPs associated with complex diseases is highly desired. Results: We propose a score-based two-stage Bayesian network method to identify causal SNPs of complex diseases for case-control designs. This method combines the ideas of constraint-based methods and score-and-search methods to learn the structure of the disease-centered local Bayesian network. Simulation experiments are conducted to compare this new algorithm with several common methods that can achieve the same function. The results show that our method improves the accuracy and stability compared to several common methods. Our method based on Bayesian network theory results in lower false-positive rates when all correct loci are detected. Besides, real-world data application suggests that our algorithm has good performance when handling genome-wide association data. Conclusion: The proposed method is designed to identify the SNPs related to complex diseases, and is more accurate than other methods which can also be adapted to large-scale genome-wide analysis studies data.

Linear high-dimensional mediation models adjusting for confounders using propensity score method.

High-dimensional mediation analysis has been developed to study whether epigenetic phenotype in a high-dimensional data form would mediate the causal pathway of exposure to disease. However, most existing models are designed based on the assumption that there are no confounders between the exposure, the mediators, and the outcome. In practice, this assumption may not be feasible since high-dimensional mediation analysis (HIMA) tends to be observational where a randomized controlled trial (RCT) cannot be conducted for some economic or ethical reasons. Thus, to deal with the confounders in HIMA cases, we proposed three propensity score-related approaches named PSR (propensity score regression), PSW (propensity score weighting), and PSU (propensity score union) to adjust for the confounder bias in HIMA, and compared them with the traditional covariate regression method. The procedures mainly include four parts: calculating the propensity score, sure independence screening, MCP (minimax concave penalty) variable selection, and joint-significance testing. Simulation results show that the PSU model is the most recommended. Applying our models to the TCGA lung cancer dataset, we find that smoking may lead to lung disease through the mediation effect of some specific DNA-methylation sites, including site Cg24480765 in gene RP11-347H15.2 and site Cg22051776 in gene KLF3.

Particulate matter of air pollution may increase risk of kidney failure in IgA nephropathy.

IgA nephropathy (IgAN) is characterized by deposition of galactose-deficient IgA1 (Gd-IgA1) in glomerular mesangium associated with mucosal immune disorders. Since environmental pollution has been associated with the progression of chronic kidney disease in the general population, we specifically investigated the influence of exposure to fine particulate matter less than 2.5 µm in diameter (PM2.5) on IgAN progression. Patients with biopsy-proven primary IgAN were recruited from seven Chinese kidney centers. PM2.5 exposure from 1998 to 2016 was derived from satellite aerosol optical depth data and a total of 1,979 patients with IgAN, including 994 males were enrolled. The PM2.5 exposure levels for patients from different provinces varied but, in general, the PM2.5 exposure levels among patients from the north were higher than those among patients from the south. The severity of PM2.5 exposure in different regions was correlated with regional kidney failure burden. In addition, each 10 µg/m3 increase in annual average concentration of PM2.5 exposure before study entry (Hazard Ratio, 1.14; 95% confidence interval, 1.06-1.22) or time-varying PM2.5 exposure after study entry (1.10; 1.01-1.18) were associated with increased kidney failure risk after adjustment for age, gender, estimated glomerular filtration rate, urine protein, uric acid, hemoglobin, mean arterial pressure, Oxford classification, glucocorticoid and renin-angiotensin system blocker therapy. The associations were robust when the time period, risk factors of cardiovascular diseases or city size were further adjusted on the basis of the above model. Thus, our results suggest that PM2.5 is an independent risk factor for kidney failure in patients with IgAN, but these findings will require validation in more diverse populations and other geographic regions.

BOB: Bayesian optimal design for biosimilar trials with co-primary endpoints.

For regulatory approval of a biosimilar product, extensive evaluations should be performed by rigorous clinical trials to establish the similarity between the reference product and the proposed biosimilar in terms of both efficacy and safety. Existing designs for biosimilar trials often use a single primary efficacy endpoint in trial monitoring, and then separately evaluate the safety of the biosimilar product in a secondary analysis at the trial completion. However, ignoring the safety endpoint and the correlation between safety and efficacy in trial monitoring may lead to a high false positive rate, or it may delay the termination of the trial when dissimilarity in safety is early detected. We propose a Bayesian optimal design for biosimilar trials by incorporating both safety and efficacy endpoints in a unified framework. Based on a Bayesian joint safety and efficacy model, we sequentially use a so-called Bayesian biosimilar probability to make go/no-go decisions. We calibrate the Bayesian design to maximize the statistical power while maintaining the frequentist type I error rate at the nominal level. We carry out extensive simulation studies to show that the design has desirable performance in terms of the false positive rate and the average sample size. We also apply the proposed design to a biosimilar trial evaluating a ranibizumab product.