Meta-analysis reveals differential gene expression in tetralogy of Fallot versus controls.

OBJECTIVES: Tetralogy of Fallot (TOF) is the most common cyanotic congenital heart defect in the United States. We aimed to identify genetic variations associated with TOF using meta-analysis of publicly available digital samples to spotlight targets for prevention, screening, and treatment strategies. METHODS: We used the Search Tag Analyze Resource for Gene Expression Omnibus (STARGEO) platform to identify 39 TOF and 19 non-TOF right ventricle tissue samples from microarray data and identified upregulated and downregulated genes. Associated gene expression data were analyzed using ingenuity pathway analysis and restricted to genes with a statistically significant (p < .05) difference and an absolute experimental log ratio >0.1 between disease and control samples. RESULTS: Our analysis identified 220 genes whose expression profiles were significantly altered in TOF vs. non-TOF samples. The most striking differences identified in gene expression included genes FBXO32, PTGES, MYL12a, and NR2F2. Some top associated canonical pathways included adrenergic signaling, estrogen receptor signaling, and the role of NFAT in cardiac hypertrophy. In general, genes involved in adaptive, defensive, and reparative cardiovascular responses showed altered expression in TOF vs. non-TOF samples. CONCLUSIONS: We introduced the interpretation of open "big data" using the STARGEO platform to define robust genomic signatures of congenital heart disease pathology of TOF. Overall, our meta-analysis results indicated increased metabolism, inflammation, and altered gene expression in TOF patients. Estrogen receptor signaling and the role of NFAT in cardiac hypertrophy represent unique pathways upregulated in TOF patients and are potential targets for future pharmacologic treatments.

Characterization of monoclonal gammopathy of undetermined significance progression to multiple myeloma through meta-analysis of GEO data.

The etiology of monoclonal gammopathy of undetermined significance (MGUS) and multiple myeloma (MM) is still obscure as are the processes that enable the progression of MGUS to MM. Understanding the unique vs. shared transcriptomes can potentially elucidate why individuals develop one or the other. Furthermore, highlighting key pathways and genes involved in the pathogenesis of MM or the development of MGUS to MM may allow the discovery of novel drug targets and therapies. We employed STARGEO platform to perform three separate meta-analysis to compare MGUS and MM transcriptomes. For these analyses we tagged (1) 101 MGUS patient plasma cells from bone marrow samples and 64 plasma cells from healthy controls (2) 383 MM patient CD138+ cells from bone marrow and the 101 MGUS samples in the first analysis as controls (3) 517 MM patient peripheral blood samples and 97 peripheral blood samples from healthy controls. We then utilized Ingenuity Pathway Analysis (IPA) to analyze the unique genomic signatures within and across these samples. Our study identified genes that may have unique roles in MGUS (GADD45RA and COMMD3), but also newly identified signaling pathways (EIF2, JAK/STAT, and MYC) and gene activity (NRG3, RBFOX2, and PARP15) in MGUS that have previously been shown to be involved in MM suggesting a spectrum of molecular overlap. On the other hand, genes such as DUSP4, RN14, LAMP5, differentially upregulated in MM, may be seen as tipping the scales from benignity to malignancy and could serve as drug targets or novel biomarkers for risk of progression. Furthermore, our analysis of MM identified newly associated gene/pathway activity such as inhibition of Wnt-signaling and defective B cell development. Finally, IPA analysis, suggests the multifactorial, oncogenic qualities of IFNγ signaling in MM may be a unifying pathway for these diverse mechanisms and prompts the need for further studies.

Predictive Models for Length of Stay and Discharge Disposition in Elective Spine Surgery: Development, Validation, and Comparison to the ACS NSQIP Risk Calculator.

STUDY DESIGN: A retrospective study at a single academic institution. OBJECTIVE: The purpose of this study is to utilize machine learning to predict hospital length of stay (LOS) and discharge disposition following adult elective spine surgery, and to compare performance metrics of machine learning models to the American College of Surgeon's National Surgical Quality Improvement Program's (ACS NSQIP) prediction calculator. SUMMARY OF BACKGROUND DATA: A total of 3678 adult patients undergoing elective spine surgery between 2014 and 2019, acquired from the electronic health record. METHODS: Patients were divided into three stratified cohorts: cervical degenerative, lumbar degenerative, and adult spinal deformity groups. Predictive variables included demographics, body mass index, surgical region, surgical invasiveness, surgical approach, and comorbidities. Regression, classification trees, and least absolute shrinkage and selection operator (LASSO) were used to build predictive models. Validation of the models was conducted on 16% of patients (N=587), using area under the receiver operator curve (AUROC), sensitivity, specificity, and correlation. Patient data were manually entered into the ACS NSQIP online risk calculator to compare performance. Outcome variables were discharge disposition (home vs. rehabilitation) and LOS (days). RESULTS: Of 3678 patients analyzed, 51.4% were male (n=1890) and 48.6% were female (n=1788). The average LOS was 3.66 days. In all, 78% were discharged home and 22% discharged to rehabilitation. Compared with NSQIP (Pearson R2 =0.16), the predictions of poisson regression ( R2 =0.29) and LASSO ( R2 =0.29) models were significantly more correlated with observed LOS ( P =0.025 and 0.004, respectively). Of the models generated to predict discharge location, logistic regression yielded an AUROC of 0.79, which was statistically equivalent to the AUROC of 0.75 for NSQIP ( P =0.135). CONCLUSION: The predictive models developed in this study can enable accurate preoperative estimation of LOS and risk of rehabilitation discharge for adult patients undergoing elective spine surgery. The demonstrated models exhibited better performance than NSQIP for prediction of LOS and equivalent performance to NSQIP for prediction of discharge location.

Gender-based time discrepancy in diagnosis of coronary artery disease based on data analytics of electronic medical records.

Background: Women continue to have worse Coronary Artery Disease (CAD) outcomes than men. The causes of this discrepancy have yet to be fully elucidated. The main objective of this study is to detect gender discrepancies in the diagnosis and treatment of CAD. Methods: We used data analytics to risk stratify ~32,000 patients with CAD of the total 960,129 patients treated at the UCSF Medical Center over an 8 year period. We implemented a multidimensional data analytics framework to trace patients from admission through treatment to create a path of events. Events are any medications or noninvasive and invasive procedures. The time between events for a similar set of paths was calculated. Then, the average waiting time for each step of the treatment was calculated. Finally, we applied statistical analysis to determine differences in time between diagnosis and treatment steps for men and women. Results: There is a significant time difference from the first time of admission to diagnostic Cardiac Catheterization between genders (p-value = 0.000119), while the time difference from diagnostic Cardiac Catheterization to CABG is not statistically significant. Conclusion: Women had a significantly longer interval between their first physician encounter indicative of CAD and their first diagnostic cardiac catheterization compared to men. Avoiding this delay in diagnosis may provide more timely treatment and a better outcome for patients at risk. Finally, we conclude by discussing the impact of the study on improving patient care with early detection and managing individual patients at risk of rapid progression of CAD.

Probing predilection to Crohn's disease and Crohn's disease flares: A crowd-sourced bioinformatics approach.

Background: Crohn's Disease (CD) is an inflammatory disease of the gastrointestinal tract that affects millions of patients. While great strides have been made in treatment, namely in biologic therapy such as anti-TNF drugs, CD remains a significant health burden. Method: We conducted two meta-analyses using our STARGEO platform to tag samples from Gene Expression Omnibus. One analysis compares inactive colonic biopsies from CD patients to colonic biopsies from healthy patients as a control and the other compares colonic biopsies from active CD lesions to inactive lesions. Separate tags were created to tag colonic samples from inflamed biopsies (total of 65 samples) and quiescent tissue in CD patients (total of 39 samples), and healthy tissue from non-CD patients (total of 30 samples). Results from the two meta-analyses were analyzed using Ingenuity Pathway Analysis. Results: For the inactive CD vs healthy tissue analysis, we noted FXR/RXR and LXR/RXR activation, superpathway of citrulline metabolism, and atherosclerosis signaling as top canonical pathways. The top upstream regulators include genes implicated in innate immunity, such as TLR3 and HNRNPA2B1, and sterol regulation through SREBF2. In addition, the sterol regulator SREBF2, lipid metabolism was the top disease network identified in IPA (Fig. 1). Top upregulated genes hold implications in innate immunity (DUOX2, REG1A/1B/3A) and cellular transport and absorption (ABCG5, NPC1L1, FOLH1, and SLC6A14). Top downregulated genes largely held roles in cell adhesion and integrity, including claudin 8, PAQR5, and PRKACB.For the active vs inactive CD analysis, we found immune cell adhesion and diapedesis, hepatic fibrosis/hepatic stellate cell activation, LPS/IL-1 inhibition of RXR function, and atherosclerosis as top canonical pathways. Top upstream regulators included inflammatory mediators LPS, TNF, IL1B, and TGFB1. Top upregulated genes function in the immune response such as IL6, CXCL1, CXCR2, MMP1/7/12, and PTGS2. Downregulated genes dealt with cellular metabolism and transport such as CPO, RBP2, G6PC, PCK1, GSTA1, and MEP1B. Conclusion: Our results build off established and recently described research in the field of CD. We demonstrate the use of our user-friendly platform, STARGEO, in investigating disease and finding therapeutic avenues.

Dissecting novel mechanisms of hepatitis B virus related hepatocellular carcinoma using meta-analysis of public data.

BACKGROUND: Hepatitis B virus (HBV) is a cause of hepatocellular carcinoma (HCC). Interestingly, this process is not necessarily mediated through cirrhosis and may in fact involve oncogenic processes. Prior studies have suggested specific oncogenic gene expression pathways were affected by viral regulatory proteins. Thus, identifying these genes and associated pathways could highlight predictive factors for HCC transformation and has implications in early diagnosis and treatment. AIM: To elucidate HBV oncogenesis in HCC and identify potential therapeutic targets. METHODS: We employed our Search, Tag, Analyze, Resource platform to conduct a meta-analysis of public data from National Center for Biotechnology Information's Gene Expression Omnibus. We performed meta-analysis consisting of 155 tumor samples compared against 185 adjacent non-tumor samples and analyzed results with ingenuity pathway analysis. RESULTS: Our analysis revealed liver X receptors/retinoid X receptor (RXR) activation and farnesoid X receptor/RXR activation as top canonical pathways amongst others. Top upstream regulators identified included the Ras family gene rab-like protein 6 (RABL6). The role of RABL6 in oncogenesis is beginning to unfold but its specific role in HBV-related HCC remains undefined. Our causal analysis suggests RABL6 mediates pathogenesis of HBV-related HCC through promotion of genes related to cell division, epigenetic regulation, and Akt signaling. We conducted survival analysis that demonstrated increased mortality with higher RABL6 expression. Additionally, homeobox A10 (HOXA10) was a top upstream regulator and was strongly upregulated in our analysis. HOXA10 has recently been demonstrated to contribute to HCC pathogenesis in vitro. Our causal analysis suggests an in vivo role through downregulation of tumor suppressors and other mechanisms. CONCLUSION: This meta-analysis describes possible roles of RABL6 and HOXA10 in the pathogenesis of HBV-related HCC. RABL6 and HOXA10 represent potential therapeutic targets and warrant further investigation.

Transcriptome changes in stages of non-alcoholic fatty liver disease.

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease in the United States and globally. The currently understood model of pathogenesis consists of a 'multiple hit' hypothesis in which environmental and genetic factors contribute to hepatic inflammation and injury. AIM: To examine the genetic expression of NAFLD and non-alcoholic steatohepatitis (NASH) tissue samples to identify common pathways that contribute to NAFLD and NASH pathogenesis. METHODS: We employed the Search Tag Analyze Resource for Gene Expression Omnibus platform to search the The National Center for Biotechnology Information Gene Expression Omnibus to elucidate NAFLD and NASH pathology. For NAFLD, we conducted meta-analysis of data from 58 NAFLD liver biopsies and 60 healthy liver biopsies; for NASH, we analyzed 187 NASH liver biopsies and 154 healthy liver biopsies. RESULTS: Our results from the NAFLD analysis reinforce the role of altered metabolism, inflammation, and cell survival in pathogenesis and support recently described contributors to disease activity, such as altered androgen and long non-coding RNA activity. The top upstream regulator was found to be sterol regulatory element binding transcription factor 1 (SREBF1), a transcription factor involved in lipid homeostasis. Downstream of SREBF1, we observed upregulation in CXCL10, HMGCR, HMGCS1, fatty acid binding protein 5, paternally expressed imprinted gene 10, and downregulation of sex hormone-binding globulin and insulin-like growth factor 1. These molecular changes reflect low-grade inflammation secondary to accumulation of fatty acids in the liver. Our results from the NASH analysis emphasized the role of cholesterol in pathogenesis. Top canonical pathways, disease networks, and disease functions were related to cholesterol synthesis, lipid metabolism, adipogenesis, and metabolic disease. Top upstream regulators included pro-inflammatory cytokines tumor necrosis factor and IL1B, PDGF BB, and beta-estradiol. Inhibition of beta-estradiol was shown to be related to derangement of several cellular downstream processes including metabolism, extracellular matrix deposition, and tumor suppression. Lastly, we found riciribine (an AKT inhibitor) and ZSTK-474 (a PI3K inhibitor) as potential drugs that targeted the differential gene expression in our dataset. CONCLUSION: In this study we describe several molecular processes that may correlate with NAFLD disease and progression. We also identified ricirbine and ZSTK-474 as potential therapy.

Data Analytics of Electronic Health Records to Enhance Care of Coronary Artery Disease in Younger Women with Avoiding Possible Delay in Treatment.

Early detection plays a key role to enhance the outcome for Coronary Artery Disease. We utilized a big data analytics platform on ∼32,000 patients to trace patients from the first encounter to CAD treatment. There are significant gender-based differences in patients younger than 60 from the time of the first encounter to Coronary Artery Bypass Grafting with a p-value=0.03. This recognition makes significant changes in outcome by avoiding delay in treatment.

An Ontology for Cardiothoracic Surgical Education and Clinical Data Analytics.

The development of an ontology facilitates the organization of the variety of concepts used to describe different terms in different resources. The proposed ontology will facilitate the study of cardiothoracic surgical education and data analytics in electronic medical records (EMR) with the standard vocabulary.

Data Analytics of Electronic Medical Record to Study Racial Diversities in Cardiovascular Diagnosis and Treatment.

The study of precision medicine that measures the effects of social, cultural, and environmental influences on health is essential to improve health outcomes. Race is a social concept used historically to divide, track, control populations, and reinforce social hierarchies. Beyond genetics, race is also a surrogate for other socioeconomic factors affecting patient outcomes. Our data analytics study aims to analyze the Electronic Medical Record (EMR) to study patients of different races in diagnosing and treating Coronary Artery Disease (CAD). We found no race discrepancies at the University of California San Francisco Medical Centers. This study opens several new hypotheses for further research in this crucial field.

Prediction of future healthcare expenses of patients from chest radiographs using deep learning: a pilot study.

Our objective was to develop deep learning models with chest radiograph data to predict healthcare costs and classify top-50% spenders. 21,872 frontal chest radiographs were retrospectively collected from 19,524 patients with at least 1-year spending data. Among the patients, 11,003 patients had 3 years of cost data, and 1678 patients had 5 years of cost data. Model performances were measured with area under the receiver operating characteristic curve (ROC-AUC) for classification of top-50% spenders and Spearman ρ for prediction of healthcare cost. The best model predicting 1-year (N = 21,872) expenditure achieved ROC-AUC of 0.806 [95% CI 0.793-0.819] for top-50% spender classification and ρ of 0.561 [0.536-0.586] for regression. Similarly, for predicting 3-year (N = 12,395) expenditure, ROC-AUC of 0.771 [0.750-0.794] and ρ of 0.524 [0.489-0.559]; for predicting 5-year (N = 1779) expenditure ROC-AUC of 0.729 [0.667-0.729] and ρ of 0.424 [0.324-0.529]. Our deep learning model demonstrated the feasibility of predicting health care expenditure as well as classifying top 50% healthcare spenders at 1, 3, and 5 year(s), implying the feasibility of combining deep learning with information-rich imaging data to uncover hidden associations that may allude to physicians. Such a model can be a starting point of making an accurate budget in reimbursement models in healthcare industries.

Empowering study of breast cancer data with application of artificial intelligence technology: promises, challenges, and use cases.

In healthcare, artificial intelligence (AI) technologies have the potential to create significant value by improving time-sensitive outcomes while lowering error rates for each patient. Diagnostic images, clinical notes, and reports are increasingly generated and stored in electronic medical records. This heterogeneous data presenting us with challenges in data analytics and reusability that is by nature has high complexity, thereby necessitating novel ways to store, manage and process, and reuse big data. This presents an urgent need to develop new, scalable, and expandable AI infrastructure and analytical methods that can enable healthcare providers to access knowledge for individual patients, yielding better decisions and outcomes. In this review article, we briefly discuss the nature of data in breast cancer study and the role of AI for generating "smart data" which offer actionable information that supports the better decision for personalized medicine for individual patients. In our view, the biggest challenge is to create a system that makes data robust and smart for healthcare providers and patients that can lead to more effective clinical decision-making, improved health outcomes, and ultimately, managing the healthcare outcomes and costs. We highlight some of the challenges in using breast cancer data and propose the need for an AI-driven environment to address them. We illustrate our vision with practical use cases and discuss a path for empowering the study of breast cancer databases with the application of AI and future directions.

Opal: an implementation science tool for machine learning clinical decision support in anesthesia.

Opal is the first published example of a full-stack platform infrastructure for an implementation science designed for ML in anesthesia that solves the problem of leveraging ML for clinical decision support. Users interact with a secure online Opal web application to select a desired operating room (OR) case cohort for data extraction, visualize datasets with built-in graphing techniques, and run in-client ML or extract data for external use. Opal was used to obtain data from 29,004 unique OR cases from a single academic institution for pre-operative prediction of post-operative acute kidney injury (AKI) based on creatinine KDIGO criteria using predictors which included pre-operative demographic, past medical history, medications, and flowsheet information. To demonstrate utility with unsupervised learning, Opal was also used to extract intra-operative flowsheet data from 2995 unique OR cases and patients were clustered using PCA analysis and k-means clustering. A gradient boosting machine model was developed using an 80/20 train to test ratio and yielded an area under the receiver operating curve (ROC-AUC) of 0.85 with 95% CI [0.80-0.90]. At the default probability decision threshold of 0.5, the model sensitivity was 0.9 and the specificity was 0.8. K-means clustering was performed to partition the cases into two clusters and for hypothesis generation of potential groups of outcomes related to intraoperative vitals. Opal's design has created streamlined ML functionality for researchers and clinicians in the perioperative setting and opens the door for many future clinical applications, including data mining, clinical simulation, high-frequency prediction, and quality improvement.

Meta-Analysis illustrates possible role of lipopolysaccharide (LPS)-induced tissue injury in nasopharyngeal carcinoma (NPC) pathogenesis.

BACKGROUND: Nasopharyngeal carcinoma (NPC) is a cancer of epithelial origin with a high incidence in certain populations. While NPC has a high remission rate with concomitant chemoradiation, recurrences are frequent, and the downstream morbidity of treatment is significant. Thus, it is imperative to find alternative therapies. METHODS: We employed a Search Tag Analyze Resource (STARGEO) platform to conduct a meta-analysis using the National Center for Biotechnology's (NCBI) Gene Expression Omnibus (GEO) to define NPC pathogenesis. We identified 111 tumor samples and 43 healthy nasopharyngeal epithelium samples from NPC public patient data. We analyzed associated signatures in Ingenuity Pathway Analysis (IPA), restricting genes that showed statistical significance (p<0.05) and an absolute experimental log ratio greater than 0.15 between disease and control samples. RESULTS: Our meta-analysis identified activation of lipopolysaccharide (LPS)-induced tissue injury in NPC tissue. Additionally, interleukin-1 (IL-1) and SB203580 were the top upstream regulators. Tumorigenesis-related genes such as homeobox A10 (HOXA10) and prostaglandin-endoperoxide synthase 2 (PTGS2 or COX-2) as well as those associated with extracellular matrix degradation, such as matrix metalloproteinases 1 and 3 (MMP-1, MMP-3) were also upregulated. Decreased expression of genes that encode proteins associated with maintaining healthy nasal respiratory epithelium structural integrity, including sentan-cilia apical structure protein (SNTN) and lactotransferrin (LTF) was documented. Importantly, we found that etanercept inhibits targets upregulated in NPC and LPS induction, such as MMP-1, PTGS2, and possibly MMP-3. CONCLUSIONS: Our analysis illustrates that nasal epithelial barrier dysregulation and maladaptive immune responses are key components of NPC pathogenesis along with LPS-induced tissue damage.

Machine Learning Prediction of Liver Allograft Utilization From Deceased Organ Donors Using the National Donor Management Goals Registry.

Early prediction of whether a liver allograft will be utilized for transplantation may allow better resource deployment during donor management and improve organ allocation. The national donor management goals (DMG) registry contains critical care data collected during donor management. We developed a machine learning model to predict transplantation of a liver graft based on data from the DMG registry. METHODS: Several machine learning classifiers were trained to predict transplantation of a liver graft. We utilized 127 variables available in the DMG dataset. We included data from potential deceased organ donors between April 2012 and January 2019. The outcome was defined as liver recovery for transplantation in the operating room. The prediction was made based on data available 12-18 h after the time of authorization for transplantation. The data were randomly separated into training (60%), validation (20%), and test sets (20%). We compared the performance of our models to the Liver Discard Risk Index. RESULTS: Of 13 629 donors in the dataset, 9255 (68%) livers were recovered and transplanted, 1519 recovered but used for research or discarded, 2855 were not recovered. The optimized gradient boosting machine classifier achieved an area under the curve of the receiver operator characteristic of 0.84 on the test set, outperforming all other classifiers. CONCLUSIONS: This model predicts successful liver recovery for transplantation in the operating room, using data available early during donor management. It performs favorably when compared to existing models. It may provide real-time decision support during organ donor management and transplant logistics.

A merged microarray meta-dataset for transcriptionally profiling colorectal neoplasm formation and progression.

Transcriptional profiling of pre- and post-malignant colorectal cancer (CRC) lesions enable temporal monitoring of molecular events underlying neoplastic progression. However, the most widely used transcriptomic dataset for CRC, TCGA-COAD, is devoid of adenoma samples, which increases reliance on an assortment of disparate microarray studies and hinders consensus building. To address this, we developed a microarray meta-dataset comprising 231 healthy, 132 adenoma, and 342 CRC tissue samples from twelve independent studies. Utilizing a stringent analytic framework, select datasets were downloaded from the Gene Expression Omnibus, normalized by frozen robust multiarray averaging and subsequently merged. Batch effects were then identified and removed by empirical Bayes estimation (ComBat). Finally, the meta-dataset was filtered for low variant probes, enabling downstream differential expression as well as quantitative and functional validation through cross-platform correlation and enrichment analyses, respectively. Overall, our meta-dataset provides a robust tool for investigating colorectal adenoma formation and malignant transformation at the transcriptional level with a pipeline that is modular and readily adaptable for similar analyses in other cancer types.

Meta-Analysis Reveals the Prognostic Relevance of Nuclear and Membrane-Associated Bile Acid Receptors in Gastric Cancer.

INTRODUCTION: Bile acids (BAs) arising from duodenogastric reflux are known to facilitate gastric cancer (GC) development. Although BAs traditionally contribute to carcinogenesis through direct cellular cytotoxicity, increasing evidence implicates nuclear and membrane BA receptors (BARs) as additional factors influencing cancer risk. Indeed, some BARs are already linked with GC, but conflicting evidence and lack of information regarding other endogenous BARs warrant further investigation. In this study, we meta-analyzed multiple data sets to identify clinically relevant relationships between BAR expression and prognosis, clinicopathology, and activity in GC. METHODS: We collected transcriptomic data from the Gene Expression Omnibus and The Cancer Genome Atlas to analyze associations between BAR expression and GC prognosis, subtype, and clinicopathology. We also used Ingenuity Pathway Analysis to assess and predict functions, upstream regulators, and downstream mediators of membrane and nuclear BARs in GC. RESULTS: BARs showed differential distribution in GC; membrane BARs (G protein-coupled BAR 1, sphingosine-1-phosphate receptor 2, and cholinergic receptor muscarinic 2) were enriched in diffuse-, genome-stable, and mesenchymal-type tumors, whereas nuclear BARs (pregnane-X-receptor, constitutive androstane receptor, and farnesoid-X-receptor) were enriched in chromosome instability and metabolic subtypes. High expression of all membrane but not nuclear BARs was associated with poor prognosis and unfavorable GC clinicopathologic features. Similarly, expression patterns of membrane but not nuclear BARs varied geographically, aligning with Helicobacter pylori infection and GC mortality rates. Finally, GC-related oncogenes, namely transforming growth factor ß1, were associated with membrane BARs, whereas many metabolic-associated genes were associated with nuclear BARs. DISCUSSION: Through transcriptomic meta-analysis, we identified distinct expression profiles between nuclear and membrane BARs that demonstrate prognostic relevance and warrant further investigation.

Metaanalysis Reveals Genetic Correlates of Osteoporosis Pathogenesis.

OBJECTIVE: Osteoporosis is a growing healthcare burden. By identifying osteoporosis-promoting genetic variations, we can spotlight targets for new pharmacologic therapies that will improve patient outcomes. In this metaanalysis, we analyzed mesenchymal stem cell (MSC) biomarkers in patients with osteoporosis. METHODS: We employed our Search Tag Analyze Resource for the Gene Expression Omnibus (STARGEO) platform to conduct a metaanalysis to define osteoporosis pathogenesis. We compared 15 osteoporotic and 14 healthy control MSC samples. We then analyzed the genetic signature in Ingenuity Pathway Analysis. RESULTS: The top canonical pathways identified that were statistically significant included the serine peptidase inhibitor kazal type 1 pancreatic cancer pathway, calcium signaling, pancreatic adenocarcinoma signaling, axonal guidance signaling, and glutamate receptor signaling. Upstream regulators involved in this disease process included ESR1, dexamethasone, CTNNß1, CREB1, and ERBB2. CONCLUSION: Although there has been extensive research looking at the genetic basis for inflammatory arthritis, very little literature currently exists that has identified genetic pathways contributing to osteoporosis. Our study has identified several important genes involved in osteoporosis pathogenesis including ESR1, CTNNß1, CREB1, and ERBB2. ESR1 has been shown to have numerous polymorphisms, which may play a prominent role in osteoporosis. The Wnt pathway, which includes the CTNNß1 gene identified in our study, plays a prominent role in bone mass regulation. Wnt pathway polymorphisms can increase susceptibility to osteoporosis. Our analysis also suggests a potential mechanism for ERBB2 in osteoporosis through Semaphorin 4D (SEMA4D). Our metaanalysis identifies several genes and pathways that can be targeted to develop new anabolic drugs for osteoporosis treatment.

A review investigating the relationship between cannabis use and adolescent cognitive functioning.

Given varying state-level laws regarding cannabis use, the objective of the review was to summarize contemporary literature on the relationship between adolescent cognitive function and academic performance with cannabis use. Frequency and quantity of cannabis use were associated with decreased functional connectivity of the brain. Earlier age at cannabis initiation and more frequent use was associated with poorer executive control and academic performance. Social determinants such as minimal parental monitoring, peer use and low social cohesion were associated with more frequent adolescent use. Race/ethnicity and residence were other factors influencing cannabis use. To prevent cannabis use disorders among adolescents, interventions should aim to prevent early initiation that can lead to chronic use in youth who may be more at risk.

The Impact of COVID-19 on African American Communities in the United States.

Purpose: The purpose of this ecological study was to understand the impact of the density of African American (AA) communities on coronavirus disease 2019 (COVID-19) prevalence and death rate within the three most populous counties in each U.S. state and territory (n=152). Methods: An ecological design was employed for the study. The top three most populous counties of each U.S. state and territory were included in analyses for a final sample size of n=152 counties. Confirmed COVID-19 cases and deaths that were accumulated between January 22, 2020 and April 12, 2020 in each of the three most populous counties in each U.S. state and territory were included. Linear regression was used to determine the association between AA density and COVID-19 prevalence (defined as the percentage of cases for the county population), and death rate (defined as number of deaths per 100,000 population). The models were adjusted for median age and poverty. Results: There was a direct association between AA density and COVID-19 prevalence; COVID-19 prevalence increased 5% for every 1% increase in county AA density (p<0.01). There was also an association between county AA density and COVID-19 deaths; the death rate increased 2 per 100,000 for every percentage increase in county AA density (p=0.02). Conclusion: These findings indicate that communities with a high AA density have been disproportionately burdened with COVID-19. To help develop effective interventions and programs that address this disparity, further study is needed to understand social determinants of health driving inequities for this community.