A Cross-sectional Survey of Comprehension and Satisfaction of Spanish-Reading Adults Regarding RÁPIDO as a Stroke Awareness Acronym.

ABSTRACT: BACKGROUND: Delay time to hospital arrival may be influenced by lack of recognition of stroke signs and the necessity to seek emergency medical, which in turn is influenced by language barriers to, a modifiable risk factor, stroke awareness education. The objective was to determine the comprehension and satisfaction of a Spanish stroke awareness acronym, RÁPIDO, among community-living, Hispanic and Latino, Spanish-reading adults. METHODS: A 33-item survey was completed by 166 adults. Data on sociodemographics, language preferences, stroke education, and comprehension and satisfaction with RÁPIDO were collected. Descriptive characteristics were calculated. Fisher exact tests were performed to determine whether reading language (group 1, only or predominantly reads in Spanish; group 2, reads in Spanish and English equally or reads predominately in English) influenced survey responses. Responses to open-ended questions were categorized. RESULTS: Sixty-nine percent of the participants were born outside of the United States, 82% currently resided in the United States, 34% read only or predominately in Spanish, and 7% had a stroke. Most participants thought RÁPIDO was informative, eye-catching, and easily remembered. Significant differences were found between reading language preference groups for correctly identifying RÁPIDO images for facial drooping (group 1, 80%; group 2, 95%; P ≤ .001) and dizziness/loss of balance (group 1, 54%; group 2, 73%; P = .027). Eighty percent or more of all participants were able to correctly interpret RÁPIDO images for facial drooping, blurry vision, impaired speech, and call emergency services. Adding "911" to the RÁPIDO image of the clock was a common suggestion. CONCLUSIONS: RÁPIDO was well received among the participants. Modifications to RÁPIDO images representing dizziness/loss of balance and arm weakness, and the addition of "911" may improve its usefulness. Obtaining more extensive feedback across the United States and testing the effect of RÁPIDO on increasing knowledge of stroke signs and retention of that knowledge are necessary next steps.

Impaired health-related quality of life in idiopathic inflammatory myopathies: a cross-sectional analysis from the COVAD-2 e-survey.

Objectives: To investigate health-related quality of life in patients with idiopathic inflammatory myopathies (IIMs) compared with those with non-IIM autoimmune rheumatic diseases (AIRDs), non-rheumatic autoimmune diseases (nrAIDs) and without autoimmune diseases (controls) using Patient-Reported Outcome Measurement Information System (PROMIS) instrument data obtained from the second COVID-19 vaccination in autoimmune disease (COVAD-2) e-survey database. Methods: Demographics, diagnosis, comorbidities, disease activity, treatments and PROMIS instrument data were analysed. Primary outcomes were PROMIS Global Physical Health (GPH) and Global Mental Health (GMH) scores. Factors affecting GPH and GMH scores in IIMs were identified using multivariable regression analysis. Results: We analysed responses from 1582 IIM, 4700 non-IIM AIRD and 545 nrAID patients and 3675 controls gathered through 23 May 2022. The median GPH scores were the lowest in IIM and non-IIM AIRD patients {13 [interquartile range (IQR) 10-15] IIMs vs 13 [11-15] non-IIM AIRDs vs 15 [13-17] nrAIDs vs 17 [15-18] controls, P < 0.001}. The median GMH scores in IIM patients were also significantly lower compared with those without autoimmune diseases [13 (IQR 10-15) IIMs vs 15 (13-17) controls, P < 0.001]. Inclusion body myositis, comorbidities, active disease and glucocorticoid use were the determinants of lower GPH scores, whereas overlap myositis, interstitial lung disease, depression, active disease, lower PROMIS Physical Function 10a and higher PROMIS Fatigue 4a scores were associated with lower GMH scores in IIM patients. Conclusion: Both physical and mental health are significantly impaired in IIM patients, particularly in those with comorbidities and increased fatigue, emphasizing the importance of patient-reported experiences and optimized multidisciplinary care to enhance well-being in people with IIMs.

Recent COVID-19 infection is associated with increased mortality in the ambulatory surgery population.

BACKGROUND: The effect of COVID-19 infection on post-operative mortality and the optimal timing to perform ambulatory surgery from diagnosis date remains unclear in this population. Our study was to determine whether a history of COVID-19 diagnosis leads to a higher risk of all-cause mortality following ambulatory surgery. METHODS: This cohort constitutes retrospective data obtained from the Optum dataset containing 44,976 US adults who were tested for COVID-19 up to 6 months before surgery and underwent ambulatory surgery between March 2020 to March 2021. The primary outcome was the risk of all-cause mortality between the COVID-19 positive and negative patients grouped according to the time interval from COVID-19 testing to ambulatory surgery, called the Testing to Surgery Interval Mortality (TSIM) of up to 6 months. Secondary outcome included determining all-cause mortality (TSIM) in time intervals of 0-15 days, 16-30 days, 31-45 days, and 46-180 days in COVID-19 positive and negative patients. RESULTS: 44,934 patients (4297 COVID-19 positive, 40,637 COVID-19 negative) were included in our analysis. COVID-19 positive patients undergoing ambulatory surgery had higher risk of all-cause mortality compared to COVID-19 negative patients (OR = 2.51, p < 0.001). The increased risk of mortality in COVID-19 positive patients remained high amongst patients who had surgery 0-45 days from date of COVID-19 testing. In addition, COVID-19 positive patients who underwent colonoscopy (OR = 0.21, p = 0.01) and plastic and orthopedic surgery (OR = 0.27, p = 0.01) had lower mortality than those underwent other surgeries. CONCLUSIONS: A COVID-19 positive diagnosis is associated with significantly higher risk of all-cause mortality following ambulatory surgery. This mortality risk is greatest in patients that undergo ambulatory surgery within 45 days of testing positive for COVID-19. Postponing elective ambulatory surgeries in patients that test positive for COVID-19 infection within 45 days of surgery date should be considered, although prospective studies are needed to assess this.

Longitudinal Morphometric Changes in the Corticospinal Tract Shape After Hemorrhagic Stroke.

Deep intracerebral hemorrhage (ICH) exerts a direct force on corticospinal tracts (CST) causing shape deformation. Using serial MRI, Generalized Procrustes Analysis (GPA), and Principal Components Analysis (PCA), we temporally evaluated the change in CST shape. Thirty-five deep ICH patients with ipsilesional-CST deformation were serially imaged on a 3T-MRI with a median imaging time of day-2 and 84 of onset. Anatomical and diffusion tensor images (DTI) were acquired. Using DTI color-coded maps, 15 landmarks were drawn on each CST and the centroids were computed in 3 dimensions. The contralesional-CST landmarks were used as a reference. The GPA outlined the shape coordinates and we superimposed the ipsilesional-CST shape at the two-time points. A multivariate PCA was applied to identify eigenvectors associated with the highest percentile of change. The first three principal components representing CST deformation along the left-right (PC1), anterior-posterior (PC2), and superior-inferior (PC3) respectively were responsible for 57.9% of shape variance. The PC1 (36.1%, p < 0.0001) and PC3 (9.58%, p < 0.01) showed a significant deformation between the two-time points. Compared to the contralesional-CST, the ipsilesional PC scores were significantly (p < 0.0001) different only at the first-timepoint. A significant positive association between the ipsilesional-CST deformation and hematoma volume was observed. We present a novel method to quantify CST deformation caused by ICH. Deformation most often occurs in left-right axis (PC1) and superior-inferior (PC3) directions. As compared to the reference, the significant temporal difference at the first time point suggests CST restoration over time.

Effect of tracheostomy timing on outcomes in patients with traumatic brain injury.

Tracheostomy following severe traumatic brain injury (TBI) is common, yet the outcomes associated with tracheostomy timing are unclear. The objective of this study was to assess hospital outcomes of tracheostomy timing in TBI patients. We retrospectively analyzed data from the National Inpatient Sample database of adult patients aged ≥18 years with a primary diagnosis of TBI. Indexed hospitalizations of TBI patients who underwent either percutaneous or surgical tracheostomy between 1995 and 2015 in the United States were included. The interventional groups were 1) early tracheostomy (≤7 days) vs standard tracheostomy (8-14 days), vs late tracheostomy (≥15 days), and 2) tracheostomy vs no tracheostomy. Propensity score matching and conditional logistic regression models were used to analyze in-hospital mortality, length of hospitalization, and in-hospital complications among TBI patients in relation to tracheostomy timing. The risk of in-hospital mortality was 35% lower in patients who underwent tracheostomy vs those who did not (odds ratio 0.65; P < 0.001). Patients who underwent early tracheostomy had a higher risk of in-hospital mortality compared to standard tracheostomy (odds ratio 1.69; P < 0.001) or late tracheostomy (odds ratio 1.80; P < 0.001). An early tracheostomy was associated with a shorter mean hospital length of stay (27 days) compared to standard (36 days) or late tracheostomy (48 days).

A novel group of genes that cause endocrine resistance in breast cancer identified by dynamic gene expression analysis.

Breast cancer (BC) is the most common type of cancer diagnosed in women. Among female cancer deaths, BC is the second leading cause of death worldwide. For estrogen receptor-positive (ER-positive) breast cancers, endocrine therapy is an effective therapeutic approach. However, in many cases, an ER-positive tumor becomes unresponsive to endocrine therapy, and tumor regrowth occurs after treatment. While some genetic mutations contribute to resistance in some patients, the underlying causes of resistance to endocrine therapy are mostly undetermined. In this study, we utilized a recently developed statistical approach to investigate the dynamic behavior of gene expression during the development of endocrine resistance and identified a novel group of genes whose time course expression significantly change during cell modelling of endocrine resistant BC development. Expression of a subset of these genes was also differentially expressed in microarray analysis of endocrine-resistant and endocrine-sensitive tumor samples. Surprisingly, a subset of those genes was also differentially genes expressed in triple-negative breast cancer (TNBC) as compared with ER-positive BC. The findings suggest shared genetic mechanisms may underlie the development of endocrine resistant BC and TNBC. Our findings identify 34 novel genes for further study as potential therapeutic targets for treatment of endocrine-resistant BC and TNBC.

Effect of Obstructive Sleep Apnea on Outcomes After Traumatic Brain Injury: A Retrospective Cohort Analysis.

BACKGROUND: Traumatic brain injury (TBI) and obstructive sleep apnea (OSA) are common in the general population and are associated with significant morbidity and mortality. The objective of this study was to assess hospital outcomes of patients with TBI with and without a pre-existing OSA diagnosis. METHODS: We retrospectively analyzed data from the National Inpatient Sample (NIS) database of adult patients aged ≥ 18 years with a primary diagnosis of TBI. In-hospital outcomes were assessed among patients with TBI with and without pre-existing OSA hospitalized between 2005 to 2015 in the United States. Propensity score matching and conditional logistic regression models were used to analyze in-hospital mortality, length of hospitalization, and in-hospital complications among patients with TBI with and without a pretrauma OSA diagnosis. RESULTS: In our TBI cohort, the overall prevalence of diagnosed OSA was 0.90%. Patients with OSA were mostly obese or morbidly obese older men with high comorbidity burden and sustained more severe head injuries yet were less likely to undergo craniotomy or craniectomy. Following propensity score matching, the odds risk (OR) of in-hospital mortality was significantly lower in the OSA group with TBI (OR 0.58; p < 0.001). Compared with the non-OSA group, patients with OSA had significantly higher risk of respiratory complications (OR 1.23) and acute heart failure (OR 1.25) and lower risk of acute myocardial infarction (OR 0.73), cardiogenic shock (OR 0.34), and packed red blood cell transfusions (OR 0.79). Patients with OSA spent on average 0.3 days less (7.4 vs. 7.7 days) hospitalized compared with the non-OSA group. CONCLUSIONS: Patients with TBI with underlying OSA diagnosis were older and had higher comorbidity burden; however, hospital mortality was lower. Pre-existing OSA may result in protective physiologic changes such as hypoxic-ischemic preconditioning especially to cardiac and neural tissues, which can provide protection following neurological trauma, which may lead to a reduction in mortality.

Association of pre-admission opioid abuse and/or dependence on major complications in traumatic brain injury (TBI) patients.

SETTING: In the last few decades, an opioid related health crisis has been a challenging problem in many countries around the world, especially the United States. Better understanding of the association of pre-admission opioid abuse and/or dependence (POAD) on specific major complications in traumatic brain injury (TBI) patients can aid the medical team in improving patient care management and outcomes. STUDY OBJECTIVE: Our goal is to assess and quantify the risk of POAD on in-hospital mortality and major complications in TBI patients. DESIGN: We conducted a retrospective study and used the National Inpatient Sample (NIS) database from 2004 to 2015 to investigate the impact of POAD on in-hospital mortality and major complications in TBI patients. We utilized propensity score matching and conditional logistic regression models, adjusted with injury severity score (ISS) and comorbidities, to obtain the adjusted odds ratios (OR). MAIN RESULTS: POAD TBI patients had lower risks of in-hospital mortality (OR 0.58; p < 0.001) and acute myocardial infarction (OR 0.53; p = 0.045), while a higher risk of respiratory (OR 1.59; p < 0.001) and neurologic complications (OR 2.54; p < 0.001), compared to non-POAD TBI patients. Additionally, POAD patients were significantly more likely to have sepsis (OR 2.16, p < 0.001), malnutrition (OR 1.56, p < 0.001), delirium (OR 2.81, p < 0.001), respiratory failure (OR 1.79, p < 0.001), and acute renal failure (OR 1.83, p < 0.001). POAD TBI patients had shorter length of hospital stay compared to non-POAD TBI patients (mean 8.0 vs 9.2 days, p < 0.001). CONCLUSIONS: POAD TBI patients have a lower in-hospital mortality, shorter duration of hospitalization and a lower risk of acute myocardial infarction, while they are more likely to have respiratory failure, delirium, sepsis, malnutrition, and acute renal failure compared to TBI patients without POAD. Prospective study is warranted to further confirm these findings.

Nonselective beta-blockers are associated with a lower risk of hepatocellular carcinoma among cirrhotic patients in the United States.

BACKGROUND: Previous studies have demonstrated an association between nonselective beta-blockers (NSBBs) and lower risk of hepatocellular carcinoma (HCC) in cirrhosis. However, there has been no population-based study investigating the risk of HCC among cirrhotic patients treated using carvedilol. AIMS: To determine the risk of HCC among cirrhotic patients with NSBBs including carvedilol. METHODS: This retrospective cohort study utilised the Cerner Health Facts database in the United States from 2000 to 2017. Kaplan-Meier estimate, Cox proportional hazards regression, and propensity score matching (PSM) were used to test the HCC risk among the carvedilol, nadolol, and propranolol groups compared with no beta-blocker group. RESULTS: The final cohort comprised 107 428 eligible patients. The 100-month cumulative HCC incidence of NSBBs was significantly lower than the no beta-blocker group (carvedilol (11.24%) vs no beta-blocker (15.69%), nadolol (27.55%) vs no beta-blocker (32.11%), and propranolol (26.17%) vs no beta-blocker (28.84%) (P values < 0.0001). NSBBs were associated with a significantly lower risk of HCC (Hazard ratio: carvedilol 0.61 (95% CI 0.51-0.73), nadolol 0.74 (95% CI 0.63-0.87), propranolol 0.75 (95% CI 0.66-0.84) after PSM in the multivariate cox analysis. In subgroup analysis, NSBBs reduced the risk of HCC in cirrhosis with complications and non-alcoholic cirrhosis. CONCLUSIONS: NSBBs, including carvedilol, were associated with a significantly decreased risk of HCC in patients with cirrhosis when compared with no beta-blocker regardless of complications status. Future randomised-controlled studies comparing the incidence of HCC among NSBBs should elucidate which NSBB would be the best option to prevent HCC in cirrhosis.

Early Versus Late Tracheostomy in Patients With Acute Traumatic Spinal Cord Injury: A Systematic Review and Meta-analysis.

BACKGROUND: Acute traumatic spinal cord injuries (SCIs) often result in impairments in respiration that may lead to a sequelae of pulmonary dysfunction, increased risk of infection, and death. The optimal timing for tracheostomy in patients with acute SCI is currently unknown. This systematic review and meta-analysis aims to assess the optimal timing of tracheostomy in SCI patients and evaluate the potential benefits of early versus late tracheostomy. METHODS: We searched Medline, PubMed, Embase, Cochrane Central, Cochrane Database of Systematic Reviews, and PsycINFO for published studies. We included studies on adults with SCI who underwent early or late tracheostomy and compared outcomes. In addition, studies that reported a concomitant traumatic brain injury were excluded. Data were extracted independently by 2 reviewers and copied into R software for analysis. A random-effects meta-analysis was performed to estimate the pooled odds ratio (OR) or mean difference (MD). RESULTS: Eight studies with a total of 1220 patients met our inclusion criteria. The mean age and gender between early and late tracheostomy groups were similar. The majority of the studies performed an early tracheostomy within 7 days from either time of injury or tracheal intubation. Patients with a cervical SCI were twice as likely to undergo an early tracheostomy (OR = 2.13; 95% confidence interval [CI], 1.24-3.64; P = .006) compared to patients with a thoracic SCI. Early tracheostomy reduced the mean intensive care unit (ICU) length of stay by 13 days (95% CI, -19.18 to -7.00; P = .001) and the mean duration of mechanical ventilation by 18.30 days (95% CI, -24.33 to -12.28; P = .001). Although the pooled risk of in-hospital mortality was lower with early tracheostomy compared to late tracheostomy, the results were not significant (OR = 0.56; 95% CI, 0.32-1.01; P = .054). In the subgroup analysis, mortality was significantly lower in the early tracheostomy group (OR = 0.27; P = .006). Finally, no differences in pneumonia between early and late tracheostomy groups were noted. CONCLUSIONS: Based on the available data, patients with early tracheostomy within the first 7 days of injury or tracheal intubation had higher cervical SCI, shorter ICU length of stay, and shorter duration of mechanical ventilation compared to late tracheostomy. The risk of in-hospital mortality may be lower following an early tracheostomy. However, due to the quality of studies and insufficient clinical data available, it is challenging to make conclusive interpretations. Future prospective trials with a larger patient population are needed to fully assess short- and long-term outcomes of tracheostomy timing following acute SCI.

An informatics research platform to make public gene expression time-course datasets reusable for more scientific discoveries.

The exponential growth of genomic/genetic data in the era of Big Data demands new solutions for making these data findable, accessible, interoperable and reusable. In this article, we present a web-based platform named Gene Expression Time-Course Research (GETc) Platform that enables the discovery and visualization of time-course gene expression data and analytical results from the NIH/NCBI-sponsored Gene Expression Omnibus (GEO). The analytical results are produced from an analytic pipeline based on the ordinary differential equation model. Furthermore, in order to extract scientific insights from these results and disseminate the scientific findings, close and efficient collaborations between domain-specific experts from biomedical and scientific fields and data scientists is required. Therefore, GETc provides several recommendation functions and tools to facilitate effective collaborations. GETc platform is a very useful tool for researchers from the biomedical genomics community to present and communicate large numbers of analysis results from GEO. It is generalizable and broadly applicable across different biomedical research areas. GETc is a user-friendly and efficient web-based platform freely accessible at http://genestudy.org/.

Machine learning prediction of the adverse outcome for nontraumatic subarachnoid hemorrhage patients.

OBJECTIVE: Subarachnoid hemorrhage (SAH) is often devastating with increased early mortality, particularly in those with presumed delayed cerebral ischemia (DCI). The ability to accurately predict survival for SAH patients during the hospital course would provide valuable information for healthcare providers, patients, and families. This study aims to utilize electronic health record (EHR) data and machine learning approaches to predict the adverse outcome for nontraumatic SAH adult patients. METHODS: The cohort included nontraumatic SAH patients treated with vasopressors for presumed DCI from a large EHR database, the Cerner Health Facts® EMR database (2000-2014). The outcome of interest was the adverse outcome, defined as death in hospital or discharged to hospice. Machine learning-based models were developed and primarily assessed by area under the receiver operating characteristic curve (AUC). RESULTS: A total of 2467 nontraumatic SAH patients (64% female; median age [interquartile range]: 56 [47-66]) who were treated with vasopressors for presumed DCI were included in the study. 934 (38%) patients died or were discharged to hospice. The model achieved an AUC of 0.88 (95% CI, 0.84-0.92) with only the initial 24 h EHR data, and 0.94 (95% CI, 0.92-0.96) after the next 24 h. INTERPRETATION: EHR data and machine learning models can accurately predict the risk of the adverse outcome for critically ill nontraumatic SAH patients. It is possible to use EHR data and machine learning techniques to help with clinical decision-making.

Vasopressor treatment and mortality following nontraumatic subarachnoid hemorrhage: a nationwide electronic health record analysis.

OBJECTIVE: Subarachnoid hemorrhage (SAH) is a devastating cerebrovascular condition, not only due to the effect of initial hemorrhage, but also due to the complication of delayed cerebral ischemia (DCI). While hypertension facilitated by vasopressors is often initiated to prevent DCI, which vasopressor is most effective in improving outcomes is not known. The objective of this study was to determine associations between initial vasopressor choice and mortality in patients with nontraumatic SAH. METHODS: The authors conducted a retrospective cohort study using a large, national electronic medical record data set from 2000-2014 to identify patients with a new diagnosis of nontraumatic SAH (based on ICD-9 codes) who were treated with the vasopressors dopamine, phenylephrine, or norepinephrine. The relationship between the initial choice of vasopressor therapy and the primary outcome, which was defined as in-hospital death or discharge to hospice care, was examined. RESULTS: In total, 2634 patients were identified with nontraumatic SAH who were treated with a vasopressor. In this cohort, the average age was 56.5 years, 63.9% were female, and 36.5% of patients developed the primary outcome. The incidence of the primary outcome was higher in those initially treated with either norepinephrine (47.6%) or dopamine (50.6%) than with phenylephrine (24.5%). After adjusting for possible confounders using propensity score methods, the adjusted OR of the primary outcome was higher with dopamine (OR 2.19, 95% CI 1.70-2.81) and norepinephrine (OR 2.24, 95% CI 1.80-2.80) compared with phenylephrine. Sensitivity analyses using different variable selection procedures, causal inference models, and machine-learning methods confirmed the main findings. CONCLUSIONS: In patients with nontraumatic SAH, phenylephrine was significantly associated with reduced mortality in SAH patients compared to dopamine or norepinephrine. Prospective randomized clinical studies are warranted to confirm this finding.

A novel approach for propensity score matching and stratification for multiple treatments: Application to an electronic health record-derived study.

Currently, methods for conducting multiple treatment propensity scoring in the presence of high-dimensional covariate spaces that result from "big data" are lacking-the most prominent method relies on inverse probability treatment weighting (IPTW). However, IPTW only utilizes one element of the generalized propensity score (GPS) vector, which can lead to a loss of information and inadequate covariate balance in the presence of multiple treatments. This limitation motivates the development of a novel propensity score method that uses the entire GPS vector to establish a scalar balancing score that, when adjusted for, achieves covariate balance in the presence of potentially high-dimensional covariates. Specifically, the generalized propensity score cumulative distribution function (GPS-CDF) method is introduced. A one-parameter power function fits the CDF of the GPS vector and a resulting scalar balancing score is used for matching and/or stratification. Simulation results show superior performance of the new method compared to IPTW both in achieving covariate balance and estimating average treatment effects in the presence of multiple treatments. The proposed approach is applied to a study derived from electronic medical records to determine the causal relationship between three different vasopressors and mortality in patients with non-traumatic aneurysmal subarachnoid hemorrhage. Results suggest that the GPS-CDF method performs well when applied to large observational studies with multiple treatments that have large covariate spaces.

Gene expression dynamic analysis reveals co-activation of Sonic Hedgehog and epidermal growth factor followed by dynamic silencing.

Aberrant activation of the Sonic Hedgehog (SHH) gene is observed in various cancers. Previous studies have shown a "cross-talk" effect between the canonical Hedgehog signaling pathway and the Epidermal Growth Factor (EGF) pathway when SHH is active in the presence of EGF. However, the precise mechanism of the cross-talk effect on the entire gene population has not been investigated. Here, we re-analyzed publicly available data to study how SHH and EGF cooperate to affect the dynamic activity of the gene population. We used genome dynamic analysis to explore the expression profiles under different conditions in a human medulloblastoma cell line. Ordinary differential equations, equipped with solid statistical and computational tools, were exploited to extract the information hidden in the dynamic behavior of the gene population. Our results revealed that EGF stimulation plays a dominant role, overshadowing most of the SHH effects. We also identified cross-talk genes that exhibited expression profiles dissimilar to that seen under SHH or EGF stimulation alone. These unique cross-talk patterns were validated in a cell culture model. These cross-talk genes identified here may serve as valuable markers to study or test for EGF co-stimulatory effects in an SHH+ environment. Furthermore, these cross-talk genes may play roles in cancer progression, thus they may be further explored as cancer treatment targets.

Modelling of hypoxia gene expression for three different cancer cell lines.

Gene dynamic analysis is essential in identifying target genes involved pathogenesis of various diseases, including cancer. Cancer prognosis is often influenced by hypoxia. We apply a multi-step pipeline to study dynamic gene expressions in response to hypoxia in three cancer cell lines: prostate (DU145), colon (HT29), and breast (MCF7) cancers. We identified 26 distinct temporal expression patterns for prostate cell line, and 29 patterns for colon and breast cell lines. The module-based dynamic networks have been developed for all three cell lines. Our analyses improve the existing results in multiple ways. It exploits the time-dependence nature of gene expression values in identifying the dynamically significant genes; hence, more key significant genes and transcription factors have been identified. Our gene network returns significant information regarding biologically important modules of genes. Furthermore, the network has potential in learning the regulatory path between transcription factors and the downstream genes. In addition, our findings suggest that changes in genes BMP6 and ARSJ expression might have a key role in the time-dependent response to hypoxia in breast cancer.

A content-based literature recommendation system for datasets to improve data reusability - A case study on Gene Expression Omnibus (GEO) datasets.

OBJECTIVE: The centrality of data to biomedical research is difficult to understate, and the same is true for the importance of the biomedical literature in disseminating empirical findings to scientific questions made on such data. But the connections between the literature and related datasets are often weak, hampering the ability of scientists to easily move between existing datasets and existing findings to derive new scientific hypotheses. This work aims to recommend relevant literature articles for datasets with the ultimate goal of increasing the productivity of researchers. Our approach to literature recommendation for datasets is a part of the dataset reusability platform developed at the University Texas Health Science Center at Houston for datasets related to gene expression. This platform incorporates datasets from Gene Expression Omnibus (GEO). An average of 34 datasets were added to GEO daily in the last five years (i.e. 2014 to 2018), demonstrating the need for automatic methods to connect these datasets with relevant literature. The relevant literature for a given dataset may describe that dataset, provide a scientific finding based on that dataset, or even describe prior and related work to the dataset's topic that is of interest to users of the dataset. MATERIALS AND METHODS: We adopt an information retrieval paradigm for literature recommendation. In our experiments, distributional semantic features are created from the title and abstract of MEDLINE articles. Then, related articles are identified for datasets in GEO. We evaluate multiple distributional methods such as TF-IDF, BM25, Latent Semantic Analysis, Latent Dirichlet Allocation, word2vec, and doc2vec. Top similar papers are recommended for each dataset using cosine similarity between the dataset's vector representation and every paper's vector representation. We also propose several novel re-ranking and normalization methods over embeddings to improve the recommendations. RESULTS: The top-performing literature recommendation technique achieved a strict precision at 10 of 0.8333 and a partial precision at 10 of 0.9000 using BM25 based on a manual evaluation of 36 datasets. Evaluation on a larger, automatically-collected benchmark shows small but consistent gains by emphasizing the similarity of dataset and article titles. CONCLUSION: This work is the first step toward developing a literature recommendation tool by recommending relevant literature for datasets. This will hopefully lead to better data reuse experience.

An optimization approach to calculating sample sizes with binary responses.

In this article, we discuss an optimization approach to the sample size question, founded on maximizing the value of information in comparison studies with binary responses. The expected value of perfect information (EVPI) is calculated and the optimal sample size is obtained by maximizing the expected net gain of sampling (ENGS), the difference between the expected value of sample information (EVSI) and the cost of conducting the trial. The data are assumed to come from two independent binomial distributions, while the parameter of interest is the difference between the two success probabilities, [Formula: see text]. To formulate our prior knowledge on the parameters, a Dirichlet prior is used. Monte Carlo integration is used in the computation and optimization of ENGS. We also compare the results of this approach with existing Bayesian methods and show how the new approach reduces the computational complexity considerably.

Bayesian sample size calculation for estimation of the difference between two binomial proportions.

In this study, we discuss a decision theoretic or fully Bayesian approach to the sample size question in clinical trials with binary responses. Data are assumed to come from two binomial distributions. A Dirichlet distribution is assumed to describe prior knowledge of the two success probabilities p1 and p2. The parameter of interest is p = p1 - p2. The optimal size of the trial is obtained by maximising the expected net benefit function. The methodology presented in this article extends previous work by the assumption of dependent prior distributions for p1 and p2.