Robust discovery of gene regulatory networks from single-cell gene expression data by Causal Inference Using Composition of Transactions.

Gene regulatory networks (GRNs) drive organism structure and functions, so the discovery and characterization of GRNs is a major goal in biological research. However, accurate identification of causal regulatory connections and inference of GRNs using gene expression datasets, more recently from single-cell RNA-seq (scRNA-seq), has been challenging. Here we employ the innovative method of Causal Inference Using Composition of Transactions (CICT) to uncover GRNs from scRNA-seq data. The basis of CICT is that if all gene expressions were random, a non-random regulatory gene should induce its targets at levels different from the background random process, resulting in distinct patterns in the whole relevance network of gene-gene associations. CICT proposes novel network features derived from a relevance network, which enable any machine learning algorithm to predict causal regulatory edges and infer GRNs. We evaluated CICT using simulated and experimental scRNA-seq data in a well-established benchmarking pipeline and showed that CICT outperformed existing network inference methods representing diverse approaches with many-fold higher accuracy. Furthermore, we demonstrated that GRN inference with CICT was robust to different levels of sparsity in scRNA-seq data, the characteristics of data and ground truth, the choice of association measure and the complexity of the supervised machine learning algorithm. Our results suggest aiming at directly predicting causality to recover regulatory relationships in complex biological networks substantially improves accuracy in GRN inference.

Mapping emergency department asthma visits to identify poor-quality housing in New Haven, CT, USA: a retrospective cohort study.

BACKGROUND: Housing conditions are a key driver of asthma incidence and severity. Previous studies have shown increased emergency department visits for asthma among residents living in poor-quality housing. Interventions to improve housing conditions have been shown to reduce emergency department visits for asthma, but identification and remediation of poor housing conditions is often delayed or does not occur. This study evaluates whether emergency department visits for asthma can be used to identify poor-quality housing to support proactive and early intervention. METHODS: We conducted a retrospective cohort study of children and adults living in and around New Haven, CT, USA, who were seen for asthma in an urban, tertiary emergency department between March 1, 2013, and Aug 31, 2017. We geocoded and mapped patient addresses to city parcels, and calculated a composite estimate of the incidence of emergency department use for asthma for each parcel (Nv × Np/log2[P], where Nv is the estimated mean number of visits per patient, Np is the number of patients, and P is the estimated population). To determine whether parcel-level emergency department use for asthma was associated with public housing inspection scores, we used regression analyses, adjusting for neighbourhood-level and individual-level factors contributing to emergency department use for asthma. Public housing complex inspection scores were obtained from standardised home inspections, which are conducted every 1-3 years for publicly funded housing. We used a sliding-window approach to estimate how far in advance of a failed inspection the model could identify elevated use of emergency departments for asthma, using the city-wide 90th percentile as a cutoff for elevated incidence. FINDINGS: 11 429 asthma-related emergency department visits from 6366 unique patients were included in the analysis. Mean patient age was 32·4 years (SD 12·8); 3836 (60·3%) patients were female, 2530 (39·7%) were male, 3461 (57·2%) were Medicaid-insured, and 2651 (41·6%) were Black. Incidence of emergency department use for asthma was strongly correlated with lower housing inspection scores (Pearson's r=-0·55 [95% CI -0·70 to -0·35], p=3·5 × 10-6), and this correlation persisted after adjustment for patient-level and neighbourhood-level demographics using a linear regression model (r=-0·54 [-0·69 to -0·33], p=7·1 × 10-6) and non-linear regression model (r=-0·44 [-0·62 to -0·21], p=3·8 × 10-4). Elevated asthma incidence rates were typically detected around a year before a housing complex failed a housing inspection. INTERPRETATION: Emergency department visits for asthma are an early indicator of failed housing inspections. This approach represents a novel method for the early identification of poor housing conditions and could help to reduce asthma-related morbidity and mortality. FUNDING: Harvard-National Institute of Environmental Health Sciences (NIEHS) Center for Environmental Health.

Using artificial intelligence techniques for COVID-19 genome analysis.

The genome of the novel coronavirus (COVID-19) disease was first sequenced in January 2020, approximately a month after its emergence in Wuhan, capital of Hubei province, China. COVID-19 genome sequencing is critical to understanding the virus behavior, its origin, how fast it mutates, and for the development of drugs/vaccines and effective preventive strategies. This paper investigates the use of artificial intelligence techniques to learn interesting information from COVID-19 genome sequences. Sequential pattern mining (SPM) is first applied on a computer-understandable corpus of COVID-19 genome sequences to see if interesting hidden patterns can be found, which reveal frequent patterns of nucleotide bases and their relationships with each other. Second, sequence prediction models are applied to the corpus to evaluate if nucleotide base(s) can be predicted from previous ones. Third, for mutation analysis in genome sequences, an algorithm is designed to find the locations in the genome sequences where the nucleotide bases are changed and to calculate the mutation rate. Obtained results suggest that SPM and mutation analysis techniques can reveal interesting information and patterns in COVID-19 genome sequences to examine the evolution and variations in COVID-19 strains respectively.

Potentials of single-cell genomics in deciphering cellular phenotypes.

Single-cell genomics, particularly single-cell transcriptome profiling by RNA sequencing have transformed the possibilities to relate genes to functions, structures, and eventually phenotypes. We can now observe changes in each cell's transcriptome and among its neighborhoods, interrogate the sequence of transcriptional events, and assess their influence on subsequent events. This paradigm shift in biology enables us to infer causal relationships in these events with high accuracy. Here we review the latest single-cell studies in plants that uncover how cellular phenotypes emerge as a result of the transcriptome process such as waves of expression, trajectories of development and responses to the environment, and spatial information. With an eye on the advances made in animal and human studies, we further highlight some of the needed areas for future research and development, including computational methods.

Echocardiographic Features of Cardiac Injury Related to COVID-19 and Their Prognostic Value: A Systematic Review.

BACKGROUND: The available information on the echocardiographic features of cardiac injury related to the novel coronavirus disease 2019 (COVID-19) and their prognostic value are scattered in the different literature. Therefore, the aim of this study was to investigate the echocardiographic features of cardiac injury related to COVID-19 and their prognostic value. METHODS: Published studies were identified through searching PubMed, Embase (Elsevier), and Google scholar databases. The search was performed using the different combinations of the keywords "echocard*," "cardiac ultrasound," "TTE," "TEE," "transtho*," or "transeso*" with "COVID-19," "sars-COV-2," "novel corona, or "2019-nCOV." Two researchers independently screened the titles and abstracts and full texts of articles to identify studies that evaluated the echocardiographic features of cardiac injury related to COVID-19 and/or their prognostic values. RESULTS: Of 783 articles retrieved from the initial search, 11 (8 cohort and 3 cross-sectional studies) met our eligibility criteria. Rates of echocardiographic abnormalities in COVID-19 patients varied across different studies as follow: RV dilatation from 15.0% to 48.9%; RV dysfunction from 3.6% to 40%; and LV dysfunction 5.4% to 40.0%. Overall, the RV abnormalities were more common than LV abnormalities. The majority of the studies showed that there was a significant association between RV abnormalities and the severe forms and death of COVID-19. CONCLUSION: The available evidence suggests that RV dilatation and dysfunction may be the most prominent echocardiographic abnormality in symptomatic patients with COVID-19, especially in those with more severe or deteriorating forms of the disease. Also, RV dysfunction should be considered as a poor prognostic factor in COVID-19 patients.

Discovery of temporal and disease association patterns in condition-specific hospital utilization rates.

Identifying temporal variation in hospitalization rates may provide insights about disease patterns and thereby inform research, policy, and clinical care. However, the majority of medical conditions have not been studied for their potential seasonal variation. The objective of this study was to apply a data-driven approach to characterize temporal variation in condition-specific hospitalizations. Using a dataset of 34 million inpatient discharges gathered from hospitals in New York State from 2008-2011, we grouped all discharges into 263 clinical conditions based on the principal discharge diagnosis using Clinical Classification Software in order to mitigate the limitation that administrative claims data reflect clinical conditions to varying specificity. After applying Seasonal-Trend Decomposition by LOESS, we estimated the periodicity of the seasonal component using spectral analysis and applied harmonic regression to calculate the amplitude and phase of the condition's seasonal utilization pattern. We also introduced four new indices of temporal variation: mean oscillation width, seasonal coefficient, trend coefficient, and linearity of the trend. Finally, K-means clustering was used to group conditions across these four indices to identify common temporal variation patterns. Of all 263 clinical conditions considered, 164 demonstrated statistically significant seasonality. Notably, we identified conditions for which seasonal variation has not been previously described such as ovarian cancer, tuberculosis, and schizophrenia. Clustering analysis yielded three distinct groups of conditions based on multiple measures of seasonal variation. Our study was limited to New York State and results may not directly apply to other regions with distinct climates and health burden. A substantial proportion of medical conditions, larger than previously described, exhibit seasonal variation in hospital utilization. Moreover, the application of clustering tools yields groups of clinically heterogeneous conditions with similar seasonal phenotypes. Further investigation is necessary to uncover common etiologies underlying these shared seasonal phenotypes.

Poorly cited articles in peer-reviewed cardiovascular journals from 1997 to 2007: analysis of 5-year citation rates.

BACKGROUND: The extent to which articles are cited is a surrogate of the impact and importance of the research conducted; poorly cited articles may identify research of limited use and potential wasted investments. We assessed trends in the rates of poorly cited articles and journals in the cardiovascular literature from 1997 to 2007. METHODS AND RESULTS: We identified original articles published in cardiovascular journals and indexed in the Scopus citation database from 1997 to 2007. We defined poorly cited articles as those with ≤5 citations in the 5 years following publication and poorly cited journals as those with >75% of journal content poorly cited. We identified 164 377 articles in 222 cardiovascular journals from 1997 to 2007. From 1997 to 2007, the number of cardiovascular articles and journals increased by 56.9% and 75.2%, respectively. Of all the articles, 75 550 (46.0%) were poorly cited, of which 25 650 (15.6% overall) had no citations. From 1997 to 2007, the proportion of poorly cited articles declined slightly (52.1%-46.2%, trend P<0.001), although the absolute number of poorly cited articles increased by 2595 (trend P<0.001). At a journal level, 44% of cardiovascular journals had more than three-fourths of the journal's content poorly cited at 5 years. CONCLUSION: Nearly half of all peer-reviewed articles published in cardiovascular journals are poorly cited 5 years after publication, and many are not cited at all. The cardiovascular literature and the number of poorly cited articles both increased substantially from 1997 to 2007. The high proportion of poorly cited articles and journals suggests inefficiencies in the cardiovascular research enterprise.

Seventeen years' experience of peritoneal dialysis in Iran: first official report of the Iranian peritoneal dialysis registry.

BACKGROUND: To facilitate planning, national renal registries provide reliable and up-to-date information on numbers of patients with end-stage renal disease (ESRD), developing trends, treatment modalities, and outcomes. To that end, the present publication represents the first official report from Iranian Peritoneal Dialysis Registry. METHODS: The prevalence, demographics, and clinical characteristics of patients on peritoneal dialysis (PD) were collected from all PD centers throughout the country. RESULTS: By the end of 2009, the prevalence of ESRD was 507 per million population in Iran. The most common renal replacement modality was hemodialysis (51.2%), followed by kidney transplantation (44.7%), and then PD (4.1%). The mean age of PD patients was 46 years, and the most common causes of ESRD were diabetes (33.5%), hypertension (24.4%), and glomerulonephritis (8.2%). Overall patient mortality was 25%, with cardiac events (46%), cerebral stroke (10%), and infection (8%) being the main causes of death. The 1-, 3-, and 5-year survivals were 89%, 64%, and 49% respectively. The most common cause of dropout was peritonitis (17.6%). Staphylococcus (coagulase-negative and S. aureus) was the most prevalent causative organism in peritonitis episodes; however, in more than 50% of episodes, a sterile culture was reported. Mean baseline serum hemoglobin and albumin were 10.7 g/dL and 3.6 g/dL respectively. CONCLUSIONS: Our registry results, representing the second largest report of PD in the Middle East, is almost comparable to available regional data. We hope that, in future, we can improve our shortcomings and lessen the gap with developed countries.

Survival analysis of Iranian patients undergoing continuous ambulatory peritoneal dialysis using cure model.

BACKGROUND: Peritoneal dialysis is one of the most prevalent types of dialysis prescribed to the patients suffering from renal failure. Studies on the factors affecting the survival of these patients have mainly used log-rank test and Cox analysis. The present study aimed to investigate the risk factors affecting short- and long term survival of patients on continuous ambulatory peritoneal dialysis (CAPD) using cure model. METHODS: The data obtained retrospectively from 20 medical centers in Iran, between 1996 and 2009. All patients with renal failure who had been treated by CAPD and followed at least 3 months were included in the study. The STATA (11.0) software and CUREREGR module were used for survival analysis using cure model. RESULTS: Totally 2006 patients were included in this study. The major reasons for renal failure were hypertension (35.4%) and diabetes (33.6%). The median of survival time was 4.8 years with a 95% confidence interval of 4.3 to 5.6 years. The percentage of long-lived patients surviving was 40% (95% CI: 32%, 47%). The analysis showed that the effect of diabetes, serum albumin level, age, diastolic blood pressure, and medical center was significant on the long-term survival of the patients. In addition, in short-term survival the effects of age, albumin, and medical center were significant. CONCLUSIONS: By improving the quality of medical care in centers, nutritional status, controlling co-morbidities can help the patients on CAPD with better health and increase their short and long term survival.