The knowledge of the causal mechanisms underlying one single system may not be sufficient to answer certain questions. One can gain additional insights from comparing and contrasting the causal ...mechanisms underlying multiple systems and uncovering consistent and distinct causal relationships. For example, discovering common molecular mechanisms among different diseases can lead to drug repurposing. The problem of comparing causal mechanisms among multiple systems is non-trivial, since the causal mechanisms are usually unknown and need to be estimated from data. If we estimate the causal mechanisms from data generated from different systems and directly compare them (the naive method), the result can be sub-optimal. This is especially true if the data generated by the different systems differ substantially with respect to their sample sizes. In this case, the quality of the estimated causal mechanisms for the different systems will differ, which can in turn affect the accuracy of the estimated similarities and differences among the systems via the naive method. To mitigate this problem, we introduced the bootstrap estimation and the equal sample size resampling estimation method for estimating the difference between causal networks. Both of these methods use resampling to assess the confidence of the estimation. We compared these methods with the naive method in a set of systematically simulated experimental conditions with a variety of network structures and sample sizes, and using different performance metrics. We also evaluated these methods on various real-world biomedical datasets covering a wide range of data designs.
Clinical trials represent a critical milestone of translational and clinical sciences. However, poor recruitment to clinical trials has been a long standing problem affecting institutions all over ...the world. One way to reduce the cost incurred by insufficient enrollment is to minimize initiating trials that are most likely to fall short of their enrollment goal. Hence, the ability to predict which proposed trials will meet enrollment goals prior to the start of the trial is highly beneficial. In the current study, we leveraged a data set extracted from ClinicalTrials.gov that consists of 46,724 U.S. based clinical trials from 1990 to 2020. We constructed 4,636 candidate predictors based on data collected by ClinicalTrials.gov and external sources for enrollment rate prediction using various state-of-the-art machine learning methods. Taking advantage of a nested time series cross-validation design, our models resulted in good predictive performance that is generalizable to future data and stable over time. Moreover, information content analysis revealed the study design related features to be the most informative feature type regarding enrollment. Compared to the performance of models built with all features, the performance of models built with study design related features is only marginally worse (AUC = 0.78 ± 0.03 vs. AUC = 0.76 ± 0.02). The results presented can form the basis for data-driven decision support systems to assess whether proposed clinical trials would likely meet their enrollment goal.
The brain is known to express many long noncoding RNAs (lncRNAs); however, whether and how these lncRNAs function in modulating synaptic stability remains unclear. Here, we report a cerebellum highly ...expressed lncRNA, Synage, regulating synaptic stability via at least two mechanisms. One is through the function of Synage as a sponge for the microRNA miR-325-3p, to regulate expression of the known cerebellar synapse organizer Cbln1. The other function is to serve as a scaffold for organizing the assembly of the LRP1-HSP90AA1-PSD-95 complex in PF-PC synapses. Although somewhat divergent in its mature mRNA sequence, the locus encoding Synage is positioned adjacent to the Cbln1 loci in mouse, rhesus macaque, and human, and Synage is highly expressed in the cerebella of all three species. Synage deletion causes a full-spectrum cerebellar ablation phenotype that proceeds from cerebellar atrophy, through neuron loss, on to synapse density reduction, synaptic vesicle loss, and finally to a reduction in synaptic activity during cerebellar development; these deficits are accompanied by motor dysfunction in adult mice, which can be rescued by AAV-mediated Synage overexpression from birth. Thus, our study demonstrates roles for the lncRNA Synage in regulating synaptic stability and function during cerebellar development.
Pontederia cordata is previously demonstrated a cadmium (Cd) tolerant plant, and also a candidate for the phytoremediation of heavy-metal-contaminated wetlands. A hydroponic experiment was used to ...investigate variations in photosynthetic gas exchange parameters, antioxidative activities, chlorophyll and secondary metabolite contents, and transcriptome in leaves of the plant exposed to 0.44 mM Cd2+ for 0 h, 24 h, and 48 h. Under Cd2+ exposure for 24 h, the plant presented a favorable photosynthesis by maintaining relatively higher antioxidant activity. Cd2+ exposure for 48 h accelerated membrane peroxidation, declined photosynthetic pigment content, and increased polyphenol oxidase activity, thus interfering with photosynthesis. The phenylpropane pathway served as a chemical rather than physical defense against Cd2+ in the plant leaves. A total of 20,998, 4743, and 4413 differentially expressed genes (DEGs) were identified in the groups of 0 h vs 24 h, 0 h vs 48 h, and 24 h vs 48 h, respectively. The primary metabolic pathways of the DEGs were mainly enriched in nitrogen metabolism, starch and sucrose metabolism, fructose and mannose metabolism, as well as pentose-phosphate pathway, contributing to a stable cell structure and function. Flavonoid biosynthesis directly or indirectly played an antioxidative role against Cd2+ in the leaves. Forty-nine transcription factor (TF) families were identified, and 8 TF families were shared among the three groups. The present study provides a theoretical foundation for investigating tolerance mechanisms of wetland plants to Cd stress in terms of secondary metabolism and transcriptional regulation.
•Phenylpropane pathway functions as a chemical defense in the plant against Cd.•Transcriptomic on mechanisms underlying Cd-tolerance in the plant was first reported.•SBP and bHLH related to flavones biosynthesis accounted for the plant Cd tolerance.•Declined physical defense of the plant to Cd was attributed to downregulated POD and CAD.
We demonstrate a data-driven approach for calculating a “causal connectome” of directed connectivity from resting-state fMRI data using a greedy adjacency search and pairwise non-Gaussian edge ...orientations. We used this approach to construct n = 442 causal connectomes. These connectomes were very sparse in comparison to typical Pearson correlation-based graphs (roughly 2.25% edge density) yet were fully connected in nearly all cases. Prominent highly connected hubs of the causal connectome were situated in attentional (dorsal attention) and executive (frontoparietal and cingulo-opercular) networks. These hub networks had distinctly different connectivity profiles: attentional networks shared incoming connections with sensory regions and outgoing connections with higher cognitive networks, while executive networks primarily connected to other higher cognitive networks and had a high degree of bidirected connectivity. Virtual lesion analyses accentuated these findings, demonstrating that attentional and executive hub networks are points of critical vulnerability in the human causal connectome. These data highlight the central role of attention and executive control networks in the human cortical connectome and set the stage for future applications of data-driven causal connectivity analysis in psychiatry.
Modern AI-based clinical decision support models owe their success in part to the very large number of predictors they use. Safe and robust decision support, especially for intervention planning, ...requires causal, not associative, relationships. Traditional methods of causal discovery, clinical trials and extracting biochemical pathways, are resource intensive and may not scale up to the number and complexity of relationships sufficient for precision treatment planning. Computational causal structure discovery (CSD) from electronic health records (EHR) data can represent a solution, however, current CSD methods fall short on EHR data. This paper presents a CSD method tailored to the EHR data. The application of the proposed methodology was demonstrated on type-2 diabetes mellitus. A large EHR dataset from Mayo Clinic was used as development cohort, and another large dataset from an independent health system, M Health Fairview, as external validation cohort. The proposed method achieved very high recall (.95) and substantially higher precision than the general-purpose methods (.84 versus .29, and .55). The causal relationships extracted from the development and external validation cohorts had a high (81%) overlap. Due to the adaptations to EHR data, the proposed method is more suitable for use in clinical decision support than the general-purpose methods.
The care of traumatized children would benefit significantly from accurate predictive models for Posttraumatic Stress Disorder (PTSD), using information available around the time of trauma. Machine ...Learning (ML) computational methods have yielded strong results in recent applications across many diseases and data types, yet they have not been previously applied to childhood PTSD. Since these methods have not been applied to this complex and debilitating disorder, there is a great deal that remains to be learned about their application. The first step is to prove the concept: Can ML methods - as applied in other fields - produce predictive classification models for childhood PTSD? Additionally, we seek to determine if specific variables can be identified - from the aforementioned predictive classification models - with putative causal relations to PTSD.
ML predictive classification methods - with causal discovery feature selection - were applied to a data set of 163 children hospitalized with an injury and PTSD was determined three months after hospital discharge. At the time of hospitalization, 105 risk factor variables were collected spanning a range of biopsychosocial domains.
Seven percent of subjects had a high level of PTSD symptoms. A predictive classification model was discovered with significant predictive accuracy. A predictive model constructed based on subsets of potentially causally relevant features achieves similar predictivity compared to the best predictive model constructed with all variables. Causal Discovery feature selection methods identified 58 variables of which 10 were identified as most stable.
In this first proof-of-concept application of ML methods to predict childhood Posttraumatic Stress we were able to determine both predictive classification models for childhood PTSD and identify several causal variables. This set of techniques has great potential for enhancing the methodological toolkit in the field and future studies should seek to replicate, refine, and extend the results produced in this study.
Hemolysis, elevated liver enzymes, and low platelets (HELLP) syndrome is a serious complication of pregnancy. Postpartum hemorrhage indicates poor prognosis of pregnant women with HELLP syndrome. The ...aim of our study is to investigate the predictive value of coagulation markers for postpartum hemorrhage of pregnant women with HELLP syndrome. In a retrospective cohort study, 106 patients who were diagnosed as pregnant women with HELLP syndrome in Peking University Third Hospital from August 2010 to January 2017 were analyzed. The demographic characters of maternal and fetus, days of hospital stay, postpartum complications, and the laboratory tests of coagulation markers within 3 days before delivery were collected. In addition, 100 healthy pregnant women were collected as a control group. The result showed that the incidence of preeclampsia in pregnant women with postpartum hemorrhage was higher than that in pregnant women without hemorrhage (P = .011). The level of fibrinogen (FIB) in postpartum hemorrhage pregnant women with HELLP syndrome was lower than that in nonpostpartum hemorrhage pregnant women with HELLP syndrome and healthy pregnant women (2.3 1.68-2.81 vs 3.64 ± 0.95, P = .000; 2.3 1.68-2.81 vs 4.48 ± 0.62, P = .000). Multivariate analysis showed that decreased FIB levels independently predicted the postpartum hemorrhage of pregnant women with HELLP syndrome (odds ratio = 7.374, 95% confidence interval CI, 1.551-35.05, P = .012). The receiver operating characteristic curve showed that the area under the curve of FIB level when predicting postpartum hemorrhage is 0.841 (95% CI, 0.708-0.976). When the cutoff value of FIB was 3.04 g/L, the sensitivity was 90.90% and the specificity was75.80%. Therefore, the low level of prenatal FIB is a reliable biomarker to predict postpartum hemorrhage of pregnant women with HELLP syndrome, which make it useful for pregnant women with HELLP syndrome in guiding surveillance therapy and prognosis assessment.
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