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Clustered or longitudinal data are commonly encountered in clinical trials and observational studies. This type of data could be collected through a real-time monitoring scheme associated with some specific event, such as disease recurrence, hospitalization, or emergency room visit. In these contexts, the cluster size could be informative because of its potential correlation with disease status, since more frequency of observations may indicate a worsening health condition. However, for some clusters/subjects, there are no measures or relevant medical records. Under such circumstances, these clusters/subjects may have a considerably lower risk of an event occurrence or may not be susceptible to such events at all, indicating a nonignorable zero cluster size. There is a substantial body of literature using observations from those clusters with a nonzero informative cluster size only, but few works discuss informative nonignorable zero-sized clusters. To utilize the information from both event-free and event-occurring participants, we propose a weighted within-cluster-resampling (WWCR) method and its asymptotically equivalent method, dual-weighted generalized estimating equations (WWGEE) by adopting the inverse probability weighting technique. The asymptotic properties are rigorously presented theoretically. Extensive simulations and an illustrative example of the Assessment, Serial Evaluation, and Subsequent Sequelae of Acute Kidney Injury (ASSESS-AKI) study are performed to analyze the finite-sample behavior of our methods and to show their advantageous performance compared to the existing approaches.

Many observational studies and clinical trials collect various secondary outcomes that may be highly correlated with the primary endpoint. These secondary outcomes are often analyzed in secondary analyses separately from the main data analysis. However, these secondary outcomes can be used to improve the estimation precision in the main analysis. We propose a method called Multiple Information Borrowing (MinBo) that borrows information from secondary data (containing secondary outcomes and covariates) to improve the efficiency of the main analysis. The proposed method is robust against model misspecification of the secondary data. Both theoretical and case studies demonstrate that MinBo outperforms existing methods in terms of efficiency gain. We apply MinBo to data from the Atherosclerosis Risk in Communities study to assess risk factors for hypertension., Comment: Contact Email: chixiang.chen@som.umaryland.edu

In many clinical and observational studies, auxiliary data from the same subjects, such as repeated measurements or surrogate variables, will be collected in addition to the data of main interest. Not directly related to the main study, these auxiliary data in practice are rarely incorporated into the main analysis, though they may carry extra information that can help improve the estimation in the main analysis. Under the setting where part of or all subjects have auxiliary data available, we propose an effective weighting approach to borrow the auxiliary information by building a working model for the auxiliary data, where improvement of estimation precision over the main analysis is guaranteed regardless of the specification of the working model. An information index is also constructed to assess how well the selected working model works to improve the main analysis. Both theoretical and numerical studies show the excellent and robust performance of the proposed method in comparison to estimation without using the auxiliary data. Finally, we utilize the Atherosclerosis Risk in Communities study for illustration.

Genome-wide association studies (GWAS) have identified and reproduced thousands of diseases associated loci, but many of them are not directly interpretable due to the strong linkage disequilibrium among variants. Transcriptome-wide association studies (TWAS) incorporated expression quantitative trait loci (eQTL) cohorts as a reference panel to detect associations with the phenotype at the gene level and have been gaining popularity in recent years. For nicotine addiction, several important susceptible genetic variants were identified by GWAS, but TWAS that detected genes associated with nicotine addiction and unveiled the underlying molecular mechanism were still lacking. In this study, we used eQTL data from the Genotype-Tissue Expression (GTEx) consortium as a reference panel to conduct tissue-specific TWAS on cigarettes per day (CPD) over thirteen brain tissues in two large cohorts: UK Biobank (UKBB; number of participants (N) = 142,202) and the GWAS & Sequencing Consortium of Alcohol and Nicotine use (GSCAN; N = 143,210), then meta-analyzing the results across tissues while considering the heterogeneity across tissues. We identified three major clusters of genes with different meta-patterns across tissues consistent in both cohorts, including homogenous genes associated with CPD in all brain tissues; partially homogeneous genes associated with CPD in cortex, cerebellum, and hippocampus tissues; and, lastly, the tissue-specific genes associated with CPD in only a few specific brain tissues. Downstream enrichment analyses on each gene cluster identified unique biological pathways associated with CPD and provided important biological insights into the regulatory mechanism of nicotine dependence in the brain.

Recent studies suggest that changes in neuronal metabolism are associated with epilepsy. High rates of ATP depletion, lactate dehydrogenase A and lactate production have all been found in epilepsy patients, animal and tissue culture models. As such, it can be hypothesized that chronic seizures lead to continuing elevations in neuronal energy demand which may lead to an adapted metabolic response and elevations of lactate dehydrogenase A. In this study, we examine elevations in the lactate dehydrogenase A protein as a long-term cellular adaptation to elevated metabolic demand from chronic neuronal activation. We investigate this cellular adaptation in human tissue samples and explore the mechanisms of lactate dehydrogenase A upregulation using cultured neurones treated with low Mg2+, a manipulation that leads to NMDA-mediated neuronal activation. We demonstrate that human epileptic tissue preferentially upregulates neuronal lactate dehydrogenase A, and that in neuronal cultures chronic and repeated elevations in neural activity lead to upregulation of neuronal lactate dehydrogenase A. Similar to states of hypoxia, this metabolic change occurs through the AMP-activated protein kinase/hypoxia-inducible factor-1α pathway. Our data therefore reveal a novel long-term bioenergetic adaptation that occurs in chronically activated neurones and provide a basis for understanding the interplay between metabolism and neural activity during epilepsy.

Triple-negative breast cancer (TNBC) patients with brain metastasis (BM) face dismal prognosis due to the limited therapeutic efficacy of the currently available treatment options. We previously demonstrated that paclitaxel-loaded PLGA-PEG nanoparticles (NPs) directed to the Fn14 receptor, termed "DARTs", are more efficacious than Abraxane─an FDA-approved paclitaxel nanoformulation─following intravenous delivery in a mouse model of TNBC BM. However, the precise basis for this difference was not investigated. Here, we further examine the utility of the DART drug delivery platform in complementary xenograft and syngeneic TNBC BM models. First, we demonstrated that, in comparison to nontargeted NPs, DART NPs exhibit preferential association with Fn14-positive human and murine TNBC cell lines cultured

The accelerated failure time (AFT) model and Cox proportional hazards (PH) model are broadly used for survival endpoints of primary interest. However, the estimation efficiency from those models can be further enhanced by incorporating the information from secondary outcomes that are increasingly available and highly correlated with primary outcomes. Those secondary outcomes could be longitudinal laboratory measures collected from doctor visits or cross-sectional disease-relevant variables, which are believed to contain extra information related to primary survival endpoints to a certain extent. In this paper, we develop a two-stage estimation framework to combine a survival model with a secondary model that contains secondary outcomes, named as the empirical-likelihood-based weighting (ELW), which comprises two weighting schemes accommodated to the AFT model (ELW-AFT) and the Cox PH model (ELW-Cox), respectively. This innovative framework is flexibly adaptive to secondary outcomes with complex data features, and it leads to more efficient parameter estimation in the survival model even if the secondary model is misspecified. Extensive simulation studies showcase more efficiency gain from ELW compared to conventional approaches, and an application in the Atherosclerosis Risk in Communities study also demonstrates the superiority of ELW by successfully detecting risk factors at the time of hospitalization for acute myocardial infarction.

Neuroimaging techniques have been increasingly used to understand the neural biology of aging brains. The neuroimaging variables from distinct brain locations and modalities can exhibit age-related patterns that reflect localized neural decline. However, it is a challenge to identify the impacts of risk factors (eg, mental disorders) on multivariate imaging variables while simultaneously accounting for the dependence structure and nonlinear age trajectories using existing tools. We propose a mixed-effects model to address this challenge by building random effects based on the latent brain aging status. We develop computationally efficient algorithms to estimate the parameters of new random effects. The simulations show that our approach provides accurate parameter estimates, improves the inference efficiency, and reduces the root mean square error compared to existing methods. We further apply this method to the UK Biobank data to investigate the effects of tobacco smoking on the white matter integrity of the entire brain during aging and identify the adverse effects on white matter integrity with multiple fiber tracts.

Graphical abstract, Recent developments in spatially resolved transcriptomics (SRT) technologies have enabled scientists to get an integrated understanding of cells in their morphological context. Applications of these technologies in diverse tissues and diseases have transformed our views of transcriptional complexity. Most published studies utilized tools developed for single-cell RNA sequencing (scRNA-seq) for data analysis. However, SRT data exhibit different properties from scRNA-seq. To take full advantage of the added dimension on spatial location information in such data, new methods that are tailored for SRT are needed. Additionally, SRT data often have companion high-resolution histology information available. Incorporating histological features in gene expression analysis is an underexplored area. In this review, we will focus on the statistical and machine learning aspects for SRT data analysis and discuss how spatial location and histology information can be integrated with gene expression to advance our understanding of the transcriptional complexity. We also point out open problems and future research directions in this field.

Background To assess: (a) cancer treatment in prostate cancer survivors (PCS) by age at diagnosis (ADx) and prostate cancer (PC) aggressiveness; (b) potential impact on PC mortality; and (c) these results in the context of environmental/behavioral risk factors on PCS in Pennsylvania. Methods Prostate cancer survivors ages ≥40 years were identified from the 2004‐2014 Pennsylvania Cancer Registry (PCR). Demographic/clinical descriptors and PC treatment were extracted from PCR. Prostate cancer aggressiveness was defined by clinical/pathologic Gleason score and tumor stage. Logistic and Cox regression analyses tested associations between treatment received and PC‐specific mortality. County‐level data from the Pennsylvania BRFSS were used to estimate cancer‐related behavioral risk factors (eg, smoking, physical inactivity, fruit/vegetable consumption [FV], alcohol use) and used as covariates. Results There were 90 694 PCS ages 40‐105 years (mean age = 66.19 years, SD = 9.25) included. Most were non‐Hispanic white men (83%). Prostate cancer survivors ≥75 years were least likely to receive any treatment but men ages 65‐74 were more likely to receive combined therapies (OR = 1.47; 95% CI 1.28, 1.69) vs PCS ages 40‐54 years, controlling for covariates. Prostate cancer survivors 55‐75+ with aggressive PC who received any treatment vs no definitive treatment had significantly reduced mortality. Men from counties with high obesity and smoking rates were significantly less likely to receive any treatment than men living in counties with lower rates of these risk factors. Prostate cancer survivors who lived in counties with high rates of physical inactivity and had high rates of sufficient FV consumption were slightly more likely to receive cancer treatment vs no definitive treatment compared to men who lived in counties with high rates of physical activity and lower FV consumption. Conclusions We observed a general age‐related decline in receipt of treatment. Prostate cancer survivors ages ≥75 years were significantly less likely to get any cancer treatment compared to younger PCS. However, most men with more aggressive disease who received any treatment had greatly reduced PC mortality, regardless of age. Considering environmental/behavioral risk factors may attenuate PC risk and inform treatment options., We analyzed prostate cancer registry data for >90 000 men ages 40‐105 to understand age‐related associations with disease aggressiveness, treatment and potential role of environmental/behavioral risk factors in understanding likelihood of treatment recommendations and impact on mortality. Men older than 75 years were more likely to have aggressive disease but less likely to receive any treatment. The oldest men also had the greatest survival benefits from any treatment. We also provide insight on whether where you live impacts your treatment and ultimate survival.

Over the past few decades, neuroimaging has become a ubiquitous tool in basic research and clinical studies of the human brain. However, no reference standards currently exist to quantify individual differences in neuroimaging metrics over time, in contrast to growth charts for anthropometric traits such as height and weight1. Here we assemble an interactive open resource to benchmark brain morphology derived from any current or future sample of MRI data ( http://www.brainchart.io/ ). With the goal of basing these reference charts on the largest and most inclusive dataset available, acknowledging limitations due to known biases of MRI studies relative to the diversity of the global population, we aggregated 123,984 MRI scans, across more than 100 primary studies, from 101,457 human participants between 115 days post-conception to 100 years of age. MRI metrics were quantified by centile scores, relative to non-linear trajectories2 of brain structural changes, and rates of change, over the lifespan. Brain charts identified previously unreported neurodevelopmental milestones3, showed high stability of individuals across longitudinal assessments, and demonstrated robustness to technical and methodological differences between primary studies. Centile scores showed increased heritability compared with non-centiled MRI phenotypes, and provided a standardized measure of atypical brain structure that revealed patterns of neuroanatomical variation across neurological and psychiatric disorders. In summary, brain charts are an essential step towards robust quantification of individual variation benchmarked to normative trajectories in multiple, commonly used neuroimaging phenotypes., R.A.I.B. was supported by a British Academy Postdoctoral fellowship and by the Autism Research Trust. J. Seidlitz was supported by NIMH T32MH019112-29 and K08MH120564. S.R.W. was funded by UKRI Medical Research Council MC_UU_00002/2 and was supported by the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014). E.T.B. was supported by an NIHR Senior Investigator award and the Wellcome Trust collaborative award for the Neuroscience in Psychiatry Network. A.F.A.-B. was supported by NIMH K08MH120564. Data were curated and analysed using a computational facility funded by an MRC research infrastructure award (MR/M009041/1) to the School of Clinical Medicine, University of Cambridge and supported by the mental health theme of the NIHR Cambridge Biomedical Research Centre.

Tobacco smoking is a risk factor for impaired brain function, but its causal effect on white matter brain aging remains unclear. This study aimed to measure the causal effect of tobacco smoking on white matter brain aging.Mendelian randomization (MR) analysis using two non-overlapping data sets (with and without neuroimaging data) from UK Biobank (UKB). The group exposed to smoking and control group consisted of current smokers and never smokers, respectively. Our main method was generalized weighted linear regression with other methods also included as sensitivity analysis.United Kingdom.The study cohort included 23 624 subjects [10 665 males and 12 959 females with a mean age of 54.18 years, 95% confidence interval (CI) = 54.08, 54.28].Genetic variants were selected as instrumental variables under the MR analysis assumptions: (1) associated with the exposure; (2) influenced outcome only via exposure; and (3) not associated with confounders. The exposure smoking status (current versus never smokers) was measured by questionnaires at the initial visit (2006-10). The other exposure, cigarettes per day (CPD), measured the average number of cigarettes smoked per day for current tobacco users over the life-time. The outcome was the 'brain age gap' (BAG), the difference between predicted brain age and chronological age, computed by training machine learning model on a non-overlapping set of never smokers.The estimated BAG had a mean of 0.10 (95% CI = 0.06, 0.14) years. The MR analysis showed evidence of positive causal effect of smoking behaviors on BAG: the effect of smoking is 0.21 (in years, 95% CI = 6.5 × 10There appears to be a significant causal effect of smoking on the brain age gap, which suggests that smoking prevention can be an effective intervention for accelerated brain aging and the age-related decline in cognitive function.

Motivation The collection of temporal or perturbed data is often a prerequisite for reconstructing dynamic networks in most cases. However, these types of data are seldom available for genomic studies in medicine, thus significantly limiting the use of dynamic networks to characterize the biological principles underlying human health and diseases. Results We proposed a statistical framework to recover disease risk-associated pseudo-dynamic networks (DRDNet) from steady-state data. We incorporated a varying coefficient model with multiple ordinary differential equations to learn a series of networks. We analyzed the publicly available Genotype-Tissue Expression data to construct networks associated with hypertension risk, and biological findings showed that key genes constituting these networks had pivotal and biologically relevant roles associated with the vascular system. We also provided the selection consistency of the proposed learning procedure and evaluated its utility through extensive simulations. Availability and implementation DRDNet is implemented in the R language, and the source codes are available at https://github.com/chencxxy28/DRDnet/. Supplementary information Supplementary data are available at Bioinformatics online.

Surfactant proteins (SPs) are important for normal lung function and innate immunity of the lungs and their genes have been identified with significant genetic variability. Changes in quantity or quality of SPs due to genetic mutations or natural genetic variability may alter their functions and contribute to the host susceptibility for particular diseases. Alternatively, SP single nucleotide polymorphisms (SNPs) can serve as markers to identify disease risk or response to therapies, as shown for other genes in a number of other studies. In the current study, we evaluated associations of SFTP SNPs with idiopathic pulmonary fibrosis (IPF) by studying novel computational models where the epistatic effects (dominant, additive, recessive) of SNP-SNP interactions could be evaluated, and then compared the results with a previously published hypersensitivity pneumonitis (HP) study where the same novel models were used. Mexican Hispanic patients (IPF=84 & HP=75) and 194 healthy control individuals were evaluated. The goal was to identify SP SNPs and SNP-SNP interactions that associate with IPF as well as SNPs and interactions that may be unique to each of these interstitial diseases or common between them. We observed: 1) in terms of IPF, i) three single SFTPA1 SNPs to associate with decreased IPF risk, ii) three SFTPA1 haplotypes to associate with increased IPF risk, and iii) a number of three-SNP interactions to associate with IPF susceptibility. 2) Comparison of IPF and HP, i) three SFTPA1 and one SFTPB SNP associated with decreased risk in IPF but increased risk in HP, and one SFTPA1 SNP associated with decreased risk in both IPF and HP, ii) a number of three-SNP interactions with the same or different effect pattern associated with IPF and/or HP susceptibility, iii) one of the three-SNP interactions that involved SNPs of SFTPA1, SFTPA2, and SFTPD, with the same effect pattern, was associated with a disease-specific outcome, a decreased and increased risk in HP and IPF, respectively. This is the first study that compares the SP gene variants in these two phenotypically similar diseases. Our findings indicate that SNPs of all SFTPs may play an important role in the genetic susceptibility to IPF and HP. Importantly, IPF and HP share some SP genetic variants, suggesting common pathophysiological mechanisms and pathways regarding surfactant biogenesis, but also some differences, highlighting the diverse underlying pathogenic mechanisms between an inflammatory-driven fibrosis (HP) and an epithelial-driven fibrosis (IPF). Alternatively, the significant SNPs identified here, along with SNPs of other genes, could serve as markers to distinguish these two devastating diseases.

The pattern of how gene co-regulation varies across tissues determines human health. However, inferring tissue-specific regulatory networks and associating them with human phenotypes represent a substantial challenge because multi-tissue projects, including the GTEx, typically contain expression data measured only at one time point from highly heterogeneous donors. Here, we implement an interdisciplinary framework for assembling and programming genomic data from multiple tissues into fully informative gene networks, encapsulated by a complete set of bi-directional, signed, and weighted interactions, from static expression data. This framework can monitor how gene networks change simultaneously across tissues and individuals, infer gene-driven inter-tissue wiring networks, compare and test topological alterations of gene/tissue networks between health states, and predict how regulatory networks evolve across spatiotemporal gradients. Our framework provides a tool to catalogue a comprehensive encyclopedia of mechanistic gene networks that walk medical researchers through tissues in each individual and through individuals for each tissue, facilitating the translation of multi-tissue data into clinical practices.

Glioma stem cells (GSCs) promote tumor progression and therapeutic resistance and exhibit remarkable bioenergetic and metabolic plasticity, a phenomenon that has been linked to their ability to escape standard and targeted therapies. However, specific mechanisms that promote therapeutic resistance have been somewhat elusive. We hypothesized that because GSCs proliferate continuously, they may require the salvage and de novo nucleotide synthesis pathways to satisfy their bioenergetic needs. Here, we demonstrate that GSCs lacking EGFR (or EGFRvIII) amplification are exquisitely sensitive to de novo pyrimidine synthesis perturbations, while GSCs that amplify EGFR are utterly resistant. Furthermore, we show that EGFRvIII promotes BAY2402234 resistance in otherwise BAY2402234 responsive GSCs. Remarkably, a novel, orally bioavailable, blood-brain-barrier penetrating, dihydroorotate dehydrogenase (DHODH) inhibitor BAY2402234 was found to abrogate GSC proliferation, block cell-cycle progression, and induce DNA damage and apoptosis. When dosed daily by oral gavage, BAY2402234 significantly impaired the growth of two different intracranial human glioblastoma xenograft models in mice. Given this observed efficacy and the previously established safety profiles in preclinical animal models and human clinical trials, the clinical testing of BAY2402234 in patients with primary glioblastoma that lacks EGFR amplification is warranted.

The so-called growth incidence curve (GIC) is a popular way to evaluate the distributional pattern of economic growth and pro-poorness of growth in development economics. The log-transforma...

Despite its critical importance to our understanding of plant growth and adaptation, the question of how environment-induced plastic response is affected genetically remains elusive. Previous studies have shown that the reaction norm of an organism across environmental index obeys the allometrical scaling law of part-whole relationships. The implementation of this phenomenon into functional mapping can characterize how quantitative trait loci (QTLs) modulate the phenotypic plasticity of complex traits to heterogeneous environments. Here, we assemble functional mapping and allometry theory through Lokta-Volterra ordinary differential equations (LVODE) into an R-based computing platform, np2 QTL, aimed to map and visualize phenotypic plasticity QTLs. Based on LVODE parameters, np2 QTL constructs a bidirectional, signed and weighted network of QTL-QTL epistasis, whose emergent properties reflect the ecological mechanisms for genotype-environment interactions over any range of environmental change. The utility of np2 QTL was validated by comprehending the genetic architecture of phenotypic plasticity via the reanalysis of published plant height data involving 3502 recombinant inbred lines of maize planted in multiple discrete environments. np2 QTL also provides a tool for constructing a predictive model of phenotypic responses in extreme environments relative to the median environment.

We determined whether calf muscle oxygen saturation (StO2) and vascular biomarkers of inflammation and oxidative stress were associated with an exercise pressor response during treadmill walking in 179 patients with symptomatic peripheral artery disease (PAD). The exercise pressor response was measured as the change in blood pressure from rest to the end of the first 2-minute treadmill stage (2 mph, 0% grade). There was a wide range in the change in systolic blood pressure (−46 to 50 mm Hg) and in diastolic blood pressure (−23 to 38 mm Hg), with mean increases of 4.3 and 1.4 mm Hg, respectively. In multiple regression analyses, significant predictors of systolic pressure included glucose ( P < .001) and insulin ( P = .039). Significant predictors of diastolic pressure included cultured endothelial cell apoptosis ( P = .019), the percentage drop in exercise calf muscle (StO2; P = .023), high-sensitivity C-reactive protein ( P = .032), and glucose ( P = .033). Higher levels in pro-inflammatory vascular biomarkers, impaired calf muscle StO2 during exercise, and elevated blood glucose were independently associated with greater exercise pressor response in patients with symptomatic PAD. The clinical implication is that exercise and nutritional interventions designed to improve inflammation, microcirculation, and glucose metabolism may also lower blood pressure during exercise in patients with symptomatic PAD.

Repeated measures are often collected in longitudinal follow-up from clinical trials and observational studies. In many situations, these measures are adherent to some specific event and are only available when it occurs; an example is serum creatinine from laboratory tests for hospitalized acute kidney injuries. The frequency of event recurrences is potentially correlated with overall health condition and hence may influence the distribution of the outcome measure of interest, leading to informative cluster size. In particular, there may be a large portion of subjects without any events, thus no longitudinal measures are available, which may be due to insusceptibility to such events or censoring before any events, and this zero-inflation nature of the data needs to be taken into account. On the other hand, there often exists a terminal event that may be correlated with the recurrent events. Previous work in this area suffered from the limitation that not all these issues were handled simultaneously. To address this deficiency, we propose a novel joint modeling approach for longitudinal data adjusting for zero-inflated and informative cluster size as well as a terminal event. A three-stage semiparametric likelihood-based approach is applied for parameter estimation and inference. Extensive simulations are conducted to evaluate the performance of our proposal. Finally, we utilize the Assessment, Serial Evaluation, and Subsequent Sequelae of Acute Kidney Injury (ASSESS-AKI) study for illustration.

The association of the receptor binding domain (RBD) of SARS-CoV-2 viral spike with human angiotensin converting enzyme (hACE2) represents the first required step for viral entry. Amino acid changes in the RBD have been implicated with increased infectivity and potential for immune evasion. Reliably predicting the effect of amino acid changes in the ability of the RBD to interact more strongly with the hACE2 receptor can help assess the public health implications and the potential for spillover and adaptation into other animals. Here, we introduce a two-step framework that first relies on 48 independent 4-ns molecular dynamics (MD) trajectories of RBD-hACE2 variants to collect binding energy terms decomposed into Coulombic, covalent, van der Waals, lipophilic, generalized Born electrostatic solvation, hydrogen-bonding, π-π packing and self-contact correction terms. The second step implements a neural network to classify and quantitatively predict binding affinity using the decomposed energy terms as descriptors. The computational base achieves an accuracy of 82.2% in terms of correctly classifying single amino-acid substitution variants of the RBD as worsening or improving binding affinity for hACE2 and a correlation coefficient r of 0.69 between predicted and experimentally calculated binding affinities. Both metrics are calculated using a 5-fold cross validation test. Our method thus sets up a framework for effectively screening binding affinity change with unknown single and multiple amino-acid changes. This can be a very valuable tool to predict host adaptation and zoonotic spillover of current and future SARS-CoV-2 variants.

SummaryApoptosis is an integral physiological cell death process that occurs frequently and generates a huge number of apoptotic extracellular vesicles (apoEVs). However, whether apoEVs are necessary for maintaining organ homeostasis remains unclear. Here, we show that circulatory apoEVs engraft in liver and undergo specialized internalization by hepatocytes (HCs) based on surface signature of galactose and N-acetylgalactosamine. Furthermore, apoEVs rescue liver injury in apoptotic-deficient Fas mutant and Caspase-3 knockout mice, which is exerted by restoring the featured hepatic ploidy homeostasis. Surprisingly, apoEVs form a chimeric organelle complex with recipient Golgi apparatus via SNARE-mediated membrane interaction, which consequently facilitates microtubule organization and HC cytokinesis. Notably, through Golgi recovery and ploidy transition, apoEVs contribute to liver regeneration and protect against acute hepatic failure. Collectively, these results identify a previously unrecognized role for apoEVs and the specific mechanisms by which they safeguard liver homeostasis, and suggest the potential of apoEV-based therapy for liver disorders.

The hallmarks of pediatric acute respiratory failure (ARF) are dysregulated inflammation and surfactant dysfunction. The objective is to study association of surfactant protein (SP) genes&rsquo, single nucleotide polymorphisms (SNPs) with ARF and its morbidity: pulmonary dysfunction at discharge (PDAD), employing a single-, two-, and three-SNP interaction model. We enrolled 468 newborn controls and 248 children aged &le, 24 months with ARF, 86 developed PDAD. Using quantitative genetic principles, we tested the association of SP genes SNPs with ARF and PDAD. We observed a dominant effect of rs4715 of the SFTPC on ARF risk. In a three-SNP model, we found (a) 34 significant interactions among SNPs of SFTPA1, SFTPA2, and SFTPC associated with ARF (p = 0.000000002&ndash, 0.05), 15 and 19 of those interactions were associated with increased and decreased risk for ARF, respectively, (b) intergenic SNP&ndash, SNP interactions of both hydrophobic and hydrophilic SP genes associated with PDAD (p = 0.00002&ndash, 0.03). The majority of intra- and intergenic interactions associated with ARF involve the SFTPA2 SNPs, whereas most of the intra- and intergenic interactions associated with PDAD are of SFTPA1 SNPs. We also observed a dominant effect of haplotypes GG of SFTPA1 associated with increased and AA of SFTPC associated with decreased ARF risk (p = 0.02). To the best of our knowledge, this is the first study showing an association of complex interactions of SP genes with ARF and PDAD. Our data indicate that SP genes polymorphisms may contribute to ARF pathogenesis and subsequent PDAD and/or may serve as markers for disease susceptibility in healthy children.

Longitudinal data are very popular in practice, but they are often missing in either outcomes or time-dependent risk factors, making them highly unbalanced and complex. Missing data may contain various missing patterns or mechanisms, and how to properly handle it for unbiased and valid inference still presents a significant challenge. Here, we propose a novel semiparametric framework for analyzing longitudinal data with both missing responses and covariates that are missing at random and intermittent, a general and widely encountered situation in observational studies. Within this framework, we consider multiple robust estimation procedures based on innovative calibrated propensity scores, which offers additional relaxation of the misspecification of missing data mechanisms and shows more satisfactory numerical performance. Also, the corresponding robust information criterion on consistent variable selection for our proposed model is developed based on empirical likelihood-based methods. These advocated methods are evaluated in both theory and extensive simulation studies in a variety of situations, showing competing properties and advantages compared to the existing approaches. We illustrate the utility of our approach by analyzing the data from the HIV Epidemiology Research Study.

6-Thiopurine (6TP) is an actively prescribed drug in the treatment of various diseases ranging from Crohn’s disease and other inflammatory diseases to acute lymphocytic leukemia and non-Hodgkin’s leukemia. While 6TP has beneficial therapeutic uses, severe toxicities are also reported with its use, such as jaundice and liver toxicity. While numerous investigations into the mode in which toxicity originates has been undertaken. None have investigated the effects of inhibition towards UDP-Glucose Dehydrogenase (UDPGDH), an oxidative enzyme responsible for UDP-glucuronic acid (UDPGA) formation or UDP-Glucuronosyl transferase (UGT1A1), which is responsible for the conjugation of bilirubin with UDPGA for excretion. Failure to excrete bilirubin leads to jaundice and liver toxicity. We proposed that either 6TP or its primary oxidative excretion metabolites inhibit one or both of these enzymes, resulting in the observed toxicity from 6TP administration. Inhibition analysis of these purines revealed that 6-thiopurine has weak to no inhibition towards UDPGDH with a Ki of 288 μM with regard to varying UDP-glucose, but 6-thiouric (primary end metabolite, fully oxidized at carbon 2 and 8, and highly retained by the body) has a near six-fold increased inhibition towards UDPGDH with a Ki of 7 μM. Inhibition was also observed by 6-thioxanthine (oxidized at carbon 2) and 8-OH-6TP with Ki values of 54 and 14 μM, respectively. Neither 6-thiopurine or its excretion metabolites were shown to inhibit UGT1A1. Our results show that the C2 and C8 positions of 6TP are pivotal in said inhibition towards UDPGDH and have no effect upon UGT1A1, and that blocking C8 could lead to new analogs with reduced, if not eliminated jaundice and liver toxicities.

Craig JC, Broxterman RM, Wilcox SL, Chen C, Barstow TJ. Effect of adipose tissue thickness, muscle site, and sex on near-infrared spectroscopy derived total-[hemoglobin + myoglobin]. J Appl Physiol 123: 1571–1578, 2017. First published September 21, 2017; doi: 10.1152/japplphysiol.00207.2017 .—Adipose tissue thickness (ATT) attenuates signals from near-infrared spectroscopy (NIRS) and diminishes the absolute quantification of underlying tissues by contemporary NIRS devices. Based on the relationship between NIRS-derived total-[hemoglobin + myoglobin] (total-[Hb + Mb]) and ATT, we tested the hypotheses that the correction factor for ATT 1) is muscle site specific; 2) does not differ between men and women; and that 3) exclusion of the shortest source-detector distance from data analysis increases total-[Hb + Mb]. Fourteen healthy subjects (7 men) rested in a neutral body position (supine or prone) while measurements of total-[Hb + Mb] and ATT were taken at four muscles common to resting and exercise studies: vastus lateralis (VL), rectus femoris (RF), gastrocnemius (GS), and flexor digitorum superficialis (FDS). ATT averaged 6.0 ± 0.4 mm across all muscles. Every muscle showed a negative slope ( r2: 0.6–0.94; P < 0.01) for total-[Hb + Mb] as a function of ATT: VL (−34 μM/mm), RF (−26 μM/mm), GS (−54 μM/mm), and FDS (−33 μM/mm). The projected total-[Hb + Mb] at 0 mm ATT ( y-intercept) was 452, 372, 620, and 456 μM for VL, RF, GS, and FDS, respectively. No differences were found between the sexes within VL, RF, or FDS, but men had a greater projected total-[Hb + Mb] at 0 mm for GS (688 ± 44 vs. 552 ± 40 μM; P < 0.05). Exclusion of the shortest source-detector distance increased total-[Hb + Mb] by 12 ± 1 μM ( P < 0.05). The present findings demonstrate that total-[Hb + Mb] should be corrected for ATT using muscle site-specific factors which are not sex specific, except in the case of GS. NEW & NOTEWORTHY Near-infrared spectroscopy (NIRS) is an important tool for physiologists and clinicians. However, adipose tissue greatly attenuates the signals from these devices. Correcting for this attenuation has been suggested based on the strength of the relationship between NIRS-derived measurements and the adipose tissue thickness. We show that this relationship is unique to the muscle site of interest but may not be sex specific. Accurate quantification of underlying tissue mandates researchers correct for adipose tissue thickness.

Conventional likelihood-based information criteria for model selection rely on the distribution assumption of data. However, for complex data that are increasingly available in many scientific fields, the specification of their underlying distribution turns out to be challenging, and the existing criteria may be limited and are not general enough to handle a variety of model selection problems. Here, we propose a robust and consistent model selection criterion based upon the empirical likelihood function which is data-driven. In particular, this framework adopts plug-in estimators that can be achieved by solving external estimating equations, not limited to the empirical likelihood, which avoids potential computational convergence issues and allows versatile applications, such as generalized linear models, generalized estimating equations, penalized regressions and so on. The formulation of our proposed criterion is initially derived from the asymptotic expansion of the marginal likelihood under variable selection framework, but more importantly, the consistent model selection property is established under a general context. Extensive simulation studies confirm the out-performance of the proposal compared to traditional model selection criteria. Finally, an application to the Atherosclerosis Risk in Communities Study illustrates the practical value of this proposed framework., Comment: JSM Student Paper Award, ASA Nonparametric Section

Survival regression is commonly applied in biomedical studies or clinical trials, and evaluating their predictive performance plays an essential role for model diagnosis and selection. The presence of censored data, particularly if informative, may pose more challenges for the assessment of predictive accuracy. Existing literature mainly focuses on prediction for survival probabilities with limitation work for survival time. In this work, we focus on accuracy measures of predicted survival times adjusted for a potentially informative censoring mechanism (ie, coarsening at random (CAR); non-CAR) by adopting the technique of inverse probability of censoring weighting. Our proposed predictive metric can be adaptive to various survival regression frameworks including but not limited to accelerated failure time models and proportional hazards models. Moreover, we provide the asymptotic properties of the inverse probability of censoring weighting estimators under CAR. We consider the settings of high-dimensional data under CAR or non-CAR for extensions. The performance of the proposed method is evaluated through extensive simulation studies and analysis of real data from the Critical Assessment of Microarray Data Analysis.

Increasing evidence shows that quantitative inheritance is based on both DNA sequence and non-DNA sequence variants. However, how to simultaneously detect these variants from a mapping study has been unexplored, hampering our effort to illustrate the detailed genetic architecture of complex traits. We address this issue by developing a unified model of quantitative trait locus (QTL) mapping based on an open-pollinated design composed of randomly sampling maternal plants from a natural population and their half-sib seeds. This design forms a two-level hierarchical platform for a joint linkage-linkage disequilibrium analysis of population structure. The EM algorithm was implemented to estimate and test DNA sequence-based effects and non-DNA sequence-based effects of QTLs. We applied this model to analyze genetic mapping data from the OP design of a gymnosperm coniferous species, Torreya grandis, identifying 25 significant DNA sequence and non-DNA sequence QTLs for seedling height and diameter growth in different years. Results from computer simulation show that the unified model has good statistical properties and is powerful for QTL detection. Our model enables the tests of how a complex trait is affected differently by DNA-based effects and non-DNA sequence-based transgenerational effects, thus allowing a more comprehensive picture of genetic architecture to be charted and quantified.