Robust and accurate estimation of cellular fraction from tissue omics data via ensemble deconvolution.

MOTIVATION: Tissue-level omics data such as transcriptomics and epigenomics are an average across diverse cell types. To extract cell-type-specific (CTS) signals, dozens of cellular deconvolution methods have been proposed to infer cell-type fractions from tissue-level data. However, these methods produce vastly different results under various real data settings. Simulation-based benchmarking studies showed no universally best deconvolution approaches. There have been attempts of ensemble methods, but they only aggregate multiple single-cell references or reference-free deconvolution methods. RESULTS: To achieve a robust estimation of cellular fractions, we proposed EnsDeconv (Ensemble Deconvolution), which adopts CTS robust regression to synthesize the results from 11 single deconvolution methods, 10 reference datasets, 5 marker gene selection procedures, 5 data normalizations and 2 transformations. Unlike most benchmarking studies based on simulations, we compiled four large real datasets of 4937 tissue samples in total with measured cellular fractions and bulk gene expression from different tissues. Comprehensive evaluations demonstrated that EnsDeconv yields more stable, robust and accurate fractions than existing methods. We illustrated that EnsDeconv estimated cellular fractions enable various CTS downstream analyses such as differential fractions associated with clinical variables. We further extended EnsDeconv to analyze bulk DNA methylation data. AVAILABILITY AND IMPLEMENTATION: EnsDeconv is freely available as an R-package from https://github.com/randel/EnsDeconv. The RNA microarray data from the TRAUMA study are available and can be accessed in GEO (GSE36809). The demographic and clinical phenotypes can be shared on reasonable request to the corresponding authors. The RNA-seq data from the EVAPR study cannot be shared publicly due to the privacy of individuals that participated in the clinical research in compliance with the IRB approval at the University of Pittsburgh. The RNA microarray data from the FHS study are available from dbGaP (phs000007.v32.p13). The RNA-seq data from ROS study is downloaded from AD Knowledge Portal. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A comparison of the role of aggression in the association between hostile interpretation bias and antisocial personality features between young offenders and university students.

BACKGROUND: Antisocial personality features in adolescents are frequently associated with delinquency and constitute the problem that most concerns the criminal justice system and the public. Hostile interpretation bias has been identified as a candidate for explaining emergent adolescent antisocial personality problems and aggression, but it is unclear whether offenders and non-offenders show differences in the relationships between hostile interpretation bias, aggression and antisocial personality features. AIMS: To compare relationships between hostile interpretation bias and a personality measure between incarcerated teenagers and first year university students and to explore aggression and criminal history as mediating or moderating variables. METHODS: Fifty-three 16-18-year-old incarcerated male offenders and 69 17-20-year-old male university students were recruited, the former through institutional staff and the latter by online advert only. Individuals in both groups self-rated, in private, on the Word and Sentence Association Paradigm-hostile (WSAP), The Ambiguous Intentions Hostility Questionnaire (AIHQ), Hostility Interpretation Bias Task (HIBT) as tests for hostile interpretation bias, and on the Buss-Perry Aggression Questionnaire and on Hyler's Personality Disorder Questionnaire (PDQ-4). Among the students, criminal history was assessed by a self-reported binary question. LASSO regressions were used to test inter-relationships between hostile interpretation bias and aggression or antisocial personality traits. Mediation and moderation were tested using MPLUS 7.4. RESULTS: The WSAP and AIHQ, as measures of self-reported hostility bias, had relationships with self-reported aggression (Pearson r 0.24-0.58, p < 0.001) and with antisocial personality features (r 0.36-0.50, p < 0.001), the HIBT did not. Aggression scores mediated the relationship between hostile interpretation bias and antisocial personality features. Furthermore, the relationship between hostile interpretation bias and aggression was stronger among the young offenders (estimates 0.43-0.75) than among the university students without criminal history (estimates 0.13-0.36). CONCLUSIONS: Hostile interpretation bias appears to promote antisocial personality features by increasing an individual's aggression, regardless of social status, although the effect was much stronger among the young offenders. To reduce young people's antisocial personality features, future studies should perhaps focus on evaluating strategies to reduce hostile bias or prevent it from being expressed in aggressive behaviours.

A practical oxidative C-H functionalization of N-carbamoyl tetrahydro-ß-carbolines with diverse potassium trifluoroborates.

A practical and mild metal-free oxidative C-H functionalization of N-carbamoyl tetrahydro-ß-carbolines has been reported. This reaction has excellent functional group tolerance, and exhibits a broad range of potassium trifluoroborate components, allowing for the facile C-H functionalization of electronically varied N-carbamoyl THCs in high efficiency with excellent regioselectivity.

scMD facilitates cell type deconvolution using single-cell DNA methylation references.

The proliferation of single-cell RNA-sequencing data has led to the widespread use of cellular deconvolution, aiding the extraction of cell-type-specific information from extensive bulk data. However, those advances have been mostly limited to transcriptomic data. With recent developments in single-cell DNA methylation (scDNAm), there are emerging opportunities for deconvolving bulk DNAm data, particularly for solid tissues like brain that lack cell-type references. Due to technical limitations, current scDNAm sequences represent a small proportion of the whole genome for each single cell, and those detected regions differ across cells. This makes scDNAm data ultra-high dimensional and ultra-sparse. To deal with these challenges, we introduce scMD (single cell Methylation Deconvolution), a cellular deconvolution framework to reliably estimate cell type fractions from tissue-level DNAm data. To analyze large-scale complex scDNAm data, scMD employs a statistical approach to aggregate scDNAm data at the cell cluster level, identify cell-type marker DNAm sites, and create precise cell-type signature matrixes that surpass state-of-the-art sorted-cell or RNA-derived references. Through thorough benchmarking in several datasets, we demonstrate scMD's superior performance in estimating cellular fractions from bulk DNAm data. With scMD-estimated cellular fractions, we identify cell type fractions and cell type-specific differentially methylated cytosines associated with Alzheimer's disease.

scMD: cell type deconvolution using single-cell DNA methylation references.

The proliferation of single-cell RNA sequencing data has led to the widespread use of cellular deconvolution, aiding the extraction of cell type-specific information from extensive bulk data. However, those advances have been mostly limited to transcriptomic data. With recent development in single-cell DNA methylation (scDNAm), new avenues have been opened for deconvolving bulk DNAm data, particularly for solid tissues like the brain that lack cell-type references. Due to technical limitations, current scDNAm sequences represent a small proportion of the whole genome for each single cell, and those detected regions differ across cells. This makes scDNAm data ultra-high dimensional and ultra-sparse. To deal with these challenges, we introduce scMD (single cell Methylation Deconvolution), a cellular deconvolution framework to reliably estimate cell type fractions from tissue-level DNAm data. To analyze large-scale complex scDNAm data, scMD employs a statistical approach to aggregate scDNAm data at the cell cluster level, identify cell-type marker DNAm sites, and create a precise cell-type signature matrix that surpasses state-of-the-art sorted-cell or RNA-derived references. Through thorough benchmarking in several datasets, we demonstrate scMD's superior performance in estimating cellular fractions from bulk DNAm data. With scMD-estimated cellular fractions, we identify cell type fractions and cell type-specific differentially methylated cytosines associated with Alzheimer's disease.

Accurate estimation of rare cell type fractions from tissue omics data via hierarchical deconvolution.

Bulk transcriptomics in tissue samples reflects the average expression levels across different cell types and is highly influenced by cellular fractions. As such, it is critical to estimate cellular fractions to both deconfound differential expression analyses and infer cell type-specific differential expression. Since experimentally counting cells is infeasible in most tissues and studies, in silico cellular deconvolution methods have been developed as an alternative. However, existing methods are designed for tissues consisting of clearly distinguishable cell types and have difficulties estimating highly correlated or rare cell types. To address this challenge, we propose Hierarchical Deconvolution (HiDecon) that uses single-cell RNA sequencing references and a hierarchical cell type tree, which models the similarities among cell types and cell differentiation relationships, to estimate cellular fractions in bulk data. By coordinating cell fractions across layers of the hierarchical tree, cellular fraction information is passed up and down the tree, which helps correct estimation biases by pooling information across related cell types. The flexible hierarchical tree structure also enables estimating rare cell fractions by splitting the tree to higher resolutions. Through simulations and real data applications with the ground truth of measured cellular fractions, we demonstrate that HiDecon significantly outperforms existing methods and accurately estimates cellular fractions.

Hydrogel scaffolds in the treatment of spinal cord injury: a review.

Spinal cord injury (SCI) is a disease of the central nervous system often caused by accidents, and its prognosis is unsatisfactory, with long-term adverse effects on patients' lives. The key to its treatment lies in the improvement of the microenvironment at the injury and the reconstruction of axons, and tissue repair is a promising therapeutic strategy. Hydrogel is a three-dimensional mesh structure with high water content, which has the advantages of biocompatibility, degradability, and adjustability, and can be used to fill pathological defects by injectable flowing hydrophilic material in situ to accurately adapt to the size and shape of the injury. Hydrogels mimic the natural extracellular matrix for cell colonization, guide axon extension, and act as a biological scaffold, which can be used as an excellent carrier to participate in the treatment of SCI. The addition of different materials to make composite hydrogel scaffolds can further enhance their performance in all aspects. In this paper, we introduce several typical composite hydrogels and review the research progress of hydrogel for SCI to provide a reference for the clinical application of hydrogel therapy for SCI.

Effects of digital psychological interventions on physical symptoms in cancer patients: A systematic review and meta-analysis.

PURPOSE: This meta-analysis was to assess the efficacy of digital psychological interventions to improve physical symptoms (i.e., fatigue, pain, disturbed sleep, and physical well-being) among cancer patients, as well as to evaluate the variables that possibly moderate intervention effects. METHODS: Nine databases were searched for the literature up to February 2023. Two reviewers independently conducted a quality assessment. Effect sizes were reported as the standardized mean difference (Hedge's g) and estimated using a random-effects model. RESULTS: The meta-analysis included 44 randomized clinical trials comprising 7200 adults with cancer. Digital psychological interventions were associated with significant improvements in short-term fatigue (g = -0.33; 95% CI, -0.58 to -0.07) and disturbed sleep (g = -0.36; 95% CI, -0.57 to -0.15), but with non-significant changes in pain (g = -0.23; 95% CI, -0.68 to 0.21) and physical well-being (g = 0.31; 95% CI, -0.18 to 0.80). Additionally, no alleviation in long-term physical symptoms was observed. In subgroup analysis, results suggest that the country significantly moderated the effectiveness of digital psychological interventions in alleviating fatigue. CONCLUSIONS: Digital psychological interventions can be effective for improving short-term fatigue and disturbed sleep in patients with cancer. Clinicians could consider digital psychological interventions as a possible and efficient addition to better manage some of the physical symptoms during and after cancer treatment.

Association of Self-Rated Health With Functional Limitations in Patients With CKD.

RATIONALE & OBJECTIVE: In patients with chronic kidney disease (CKD), self-rated health ("In general, how do you rate your health?") is associated with mortality. The association of self-rated health with functional status is unknown. We evaluated the association of limitations in activities of daily living (ADLs) with self-rated health and clinical correlates in a cohort of patients with CKD stages 1-5. STUDY DESIGN: Prospective cohort study. SETTING & PARTICIPANTS: Patients with CKD at a nephrology outpatient clinic in western Pennsylvania. OUTCOME: Patients participated in a survey assessing their self-rated health (5-point Likert scale) and physical (ambulation, dressing, shopping) and cognitive (executive and memory) ADLs. Adjusted analysis was performed using logistic regression models. ANALYTICAL APPROACH: Logistic regression was conducted to examine the adjusted association of 3 dependent variables (sum of total, physical, and cognitive ADL limitations) with self-rated health (independent variable of interest). RESULTS: The survey was completed by 1,268 participants (mean age, 60 years; 49% females, and 74% CKD stages 3-5), of which 41% reported poor-to-fair health. Overall, 35.9% had at least 1 physical ADL limitation, 22.1% had at least 1 cognitive ADL limitation, and 12.5% had at least 3 ADL limitations. Ambulation was the most frequently reported limitation and was more common in patients reporting poor-to-fair self-rated health compared with those with good-to-excellent self-rated health (58.1% vs 17.4%, P < 0.001). In our fully adjusted model, poor-to-fair self-rated health was strongly associated with limitations in at least 3 ADLs (total ADL) [OR 8.29 (95% CI, 5.23-13.12)]. There was no significant association of eGFR with ADL limitations. LIMITATIONS: Selection bias due to optional survey completion, residual confounding, and use of abbreviated (as opposed to full) ADL questionnaires. CONCLUSIONS: Poor-to-fair self-rated health is strongly associated with physical ADL limitations in patients with CKD. Future studies should evaluate whether self-rated health questions may be useful for identifying patients who can benefit from additional evaluation and treatment of functional limitations to improve patient-centered outcomes.

Implementation of Surprise Question Assessments using the Electronic Health Record in Older Adults with Advanced CKD.

Background: The Surprise Question (SQ; "Would you be surprised if this patient died in the next 12 months?") is a validated prognostication tool for mortality and hospitalization among patients with advanced CKD. Barriers in clinical workflows have slowed SQ implementation in practice. Objectives: The aims of this study were: (1) to evaluate implementation outcomes after the use of electronic health record (EHR) decision support to automate the collection of the SQ; and (2) to assess the prognostic utility of the SQ for mortality and hospitalization/emergency room (ER) visits. Methods: We developed and implemented a best practice alert (BPA) in the EHR to identify nephrology outpatients ≥60 years of age with an eGFR <30 ml/min per 1.73 m2. At appointment, the BPA prompted the physician to answer the SQ. We assessed the rate and timeliness of provider responses. We conducted a post-hoc open-ended survey to assess physician perceptions of SQ implementation. We assessed the SQ's prognostic utility in survival and time-to-hospital encounter (hospitalization/ER visit) analyses. Results: Among 510 patients for whom the BPA triggered, 95 (19%) had the SQ completed by 16 physicians. Among those completed, nearly all (98%) were on appointment day, and 61 (64%) the first time the BPA fired. Providers answered "no" for 27 (28%) and "yes" for 68 (72%) patients. By 12 months, six (22%) "no" patients died; three (4%) "yes" patients died (hazard ratio [HR] 2.86, ref: yes, 95% CI, 1.06 to 7.69). About 35% of "no" patients and 32% of "yes" patients had a hospital encounter by 12 months (HR, 1.85, ref: yes, 95% CI, 0.93 to 3.69). Physicians noted (1) they had goals-of-care conversations unprompted; (2) EHR-based interventions alone for goals-of-care are ineffective; and (3) more robust engagement is necessary. Conclusions: We successfully integrated the SQ into the EHR to aid in clinical practice. Additional implementation efforts are needed to encourage further integration of the SQ in clinical practice.