Publications by authors named "Jiang Bian"

246 Publications

Effects of brief exposure to misinformation about e-cigarette harms on twitter: a randomised controlled experiment.

BMJ Open 2021 09 1;11(9):e045445. Epub 2021 Sep 1.

Annenberg School for Communication, University of Pennsylvania, Philadelphia, Pennsylvania, USA.

Objectives: To assess the effect of exposure to misinformation about e-cigarette harms found on Twitter on adult current smokers' intention to quit smoking cigarettes, intention to purchase e-cigarettes and perceived relative harm of e-cigarettes compared with regular cigarettes.

Setting: An online randomised controlled experiment conducted in November 2019 among USA and UK current smokers.

Participants: 2400 adult current smokers aged ≥18 years who were not current e-cigarette users recruited from an online panel. Participants' were randomised in a 1:1:1:1 ratio using a least-fill randomiser function.

Interventions: Viewing 4 tweets in random order within one of four conditions: (1) e-cigarettes are just as or more harmful than smoking, (2) e-cigarettes are completely harmless, (3) e-cigarette harms are uncertain, and (4) a control condition of tweets about physical activity.

Primary Outcomes Measures: Self-reported post-test intention to quit smoking cigarettes, intention to purchase e-cigarettes, and perceived relative harm of e-cigarettes compared with smoking.

Results: Among US and UK participants, after controlling for baseline measures of the outcome, exposure to tweets that e-cigarettes are as or more harmful than smoking versus control was associated with lower post-test intention to purchase e-cigarettes (β=-0.339, 95% CI -0.487 to -0.191, p<0.001) and increased post-test perceived relative harm of e-cigarettes (β=0.341, 95% CI 0.273 to 0.410, p<0.001). Among US smokers, exposure to tweets that e-cigarettes are completely harmless was associated with higher post-test intention to purchase e-cigarettes (β=0.229, 95% CI 0.002 to 0.456, p=0.048) and lower post-test perceived relative harm of e-cigarettes (β=-0.154, 95% CI -0.258 to -0.050, p=0.004).

Conclusions: US and UK adult current smokers may be deterred from considering using e-cigarettes after brief exposure to tweets that e-cigarettes were just as or more harmful than smoking. Conversely, US adult current smokers may be encouraged to use e-cigarettes after exposure to tweets that e-cigarettes are completely harmless. These findings suggest that misinformation about e-cigarette harms may influence some adult smokers' decisions to consider using e-cigarettes.

Trial Registration Number: ISRCTN16082420.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1136/bmjopen-2020-045445DOI Listing
September 2021

Assessing the Impact of Imputation on the Interpretations of Prediction Models: A Case Study on Mortality Prediction for Patients with Acute Myocardial Infarction.

AMIA Annu Symp Proc 2021 17;2021:465-474. Epub 2021 May 17.

Florida State University, Tallahassee, Florida, USA.

Acute myocardial infarction poses significant health risks and financial burden on healthcare and families. Prediction of mortality risk among AM! patients using rich electronic health record (EHR) data can potentially save lives and healthcare costs. Nevertheless, EHR-based prediction models usually use a missing data imputation method without considering its impact on the performance and interpretability of the model, hampering its real-world applicability in the healthcare setting. This study examines the impact of different methods for imputing missing values in EHR data on both the performance and the interpretations of predictive models. Our results showed that a small standard deviation in root mean squared error across different runs of an imputation method does not necessarily imply a small standard deviation in the prediction models' performance and interpretation. We also showed that the level of missingness and the imputation method used can have a significant impact on the interpretation of the models.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8378616PMC
September 2021

Racial, Ethnic, and Geographic Disparities in Cardiovascular Health Among Women of Childbearing Age in the United States.

J Am Heart Assoc 2021 Sep 25;10(17):e020138. Epub 2021 Aug 25.

Department of Epidemiology College of Public Health and Health Professions and College of Medicine University of Florida Gainesville FL.

Background In the United States, large disparities in cardiovascular health (CVH) exist in the general population, but little is known about the CVH status and its disparities among women of childbearing age (ie, 18-49 years). Methods and Results In this cross-sectional study, we examined racial, ethnic, and geographic disparities in CVH among all women of childbearing age in the United States, using the 2011 to 2019 Behavioral Risk Factor Surveillance System. Life's Simple 7 (ie, blood pressure, glucose, total cholesterol, smoking, body mass index, physical activity, and diet) was used to examine CVH. Women with 7 ideal CVH metrics were determined to have ideal CVH. Among the 269 564 women of childbearing age, 13 800 (4.84%) had ideal CVH. After adjusting for potential confounders, non-Hispanic Black women were less likely to have ideal CVH (odds ratio, 0.54; 95% CI, 0.46-0.63) compared with non-Hispanic White women, and with significantly lower odds of having ideal metrics of blood pressure, blood glucose, body mass index, and physical activity. No significant difference in CVH was found between non-Hispanic White and Hispanic women. Large geographic disparities with temporal variations were observed, with the age- and race-adjusted ideal CVH prevalence ranging from 4.05% in the District of Columbia (2011) to 5.55% in Maine and Montana (2019). States with low ideal CVH prevalence and average CVH score were mostly clustered in the southern United States. Conclusions Large racial, ethnic, and geographic disparities in CVH exist among women of childbearing age. More efforts are warranted to understand and address these disparities.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1161/JAHA.120.020138DOI Listing
September 2021

Transformer-Based Named Entity Recognition for Parsing Clinical Trial Eligibility Criteria.

ACM BCB 2021 Aug;2021

Florida State University.

The rapid adoption of electronic health records (EHRs) systems has made clinical data available in electronic format for research and for many downstream applications. Electronic screening of potentially eligible patients using these clinical databases for clinical trials is a critical need to improve trial recruitment efficiency. Nevertheless, manually translating free-text eligibility criteria into database queries is labor intensive and inefficient. To facilitate automated screening, free-text eligibility criteria must be structured and coded into a computable format using controlled vocabularies. Named entity recognition (NER) is thus an important first step. In this study, we evaluate 4 state-of-the-art transformer-based NER models on two publicly available annotated corpora of eligibility criteria released by Columbia University (i.e., the Chia data) and Facebook Research (i.e.the FRD data). Four transformer-based models (i.e., BERT, ALBERT, RoBERTa, and ELECTRA) pretrained with general English domain corpora vs. those pretrained with PubMed citations, clinical notes from the MIMIC-III dataset and eligibility criteria extracted from all the clinical trials on ClinicalTrials.gov were compared. Experimental results show that RoBERTa pretrained with MIMIC-III clinical notes and eligibility criteria yielded the highest strict and relaxed F-scores in both the Chia data (i.e., 0.658/0.798) and the FRD data (i.e., 0.785/0.916). With promising NER results, further investigations on building a reliable natural language processing (NLP)-assisted pipeline for automated electronic screening are needed.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1145/3459930.3469560DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8373041PMC
August 2021

Propensity score synthetic augmentation matching using generative adversarial networks (PSSAM-GAN).

Comput Methods Programs Biomed Update 2021 16;1. Epub 2021 Jul 16.

Department of Epidemiology, College of Public Health and Health Professions & College of Medicine, University of Florida, Florida 32610, USA.

Understanding causality is of crucial importance in biomedical sciences, where developing prediction models is insufficient because the models need to be actionable. However, data sources, such as electronic health records, are observational and often plagued with various types of biases, e.g. confounding. Although randomized controlled trials are the gold standard to estimate the causal effects of treatment interventions on health outcomes, they are not always possible. Propensity score matching (PSM) is a popular statistical technique for observational data that aims at balancing the characteristics of the population assigned either to a treatment or to a control group, making treatment assignment and outcome independent upon these characteristics. However, matching subjects can reduce the sample size. Inverse probability weighting (IPW) maintains the sample size, but extreme values can lead to instability. While PSM and IPW have been historically used in conjunction with linear regression, machine learning methods -including deep learning with propensity dropout- have been proposed to account for nonlinear treatment assignments. In this work, we propose a novel deep learning approach -the Propensity Score Synthetic Augmentation Matching using Generative Adversarial Networks (PSSAM-GAN)- that aims at keeping the sample size, without IPW, by generating synthetic matches. PSSAM-GAN can be used in conjunction with any other prediction method to estimate treatment effects. Experiments performed on both semi-synthetic (perinatal interventions) and real-world observational data (antibiotic treatments, and job interventions) show that the PSSAM-GAN approach effectively creates balanced datasets, relaxing the weighting/dropout needs for downstream methods, and providing competitive performance in effects estimation as compared to simple GAN and in conjunction with other deep counterfactual learning architectures, e.g. TARNet.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.cmpbup.2021.100020DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8357304PMC
July 2021

Challenges in replicating secondary analysis of electronic health records data with multiple computable phenotypes: A case study on methicillin-resistant Staphylococcus aureus bacteremia infections.

Int J Med Inform 2021 09 16;153:104531. Epub 2021 Jul 16.

Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, FL, USA. Electronic address:

Background: Replication of prediction modeling using electronic health records (EHR) is challenging because of the necessity to compute phenotypes including study cohort, outcomes, and covariates. However, some phenotypes may not be easily replicated across EHR data sources due to a variety of reasons such as the lack of gold standard definitions and documentation variations across systems, which may lead to measurement error and potential bias. Methicillin-resistant Staphylococcus aureus (MRSA) infections are responsible for high mortality worldwide. With limited treatment options for the infection, the ability to predict MRSA outcome is of interest. However, replicating these MRSA outcome prediction models using EHR data is problematic due to the lack of well-defined computable phenotypes for many of the predictors as well as study inclusion and outcome criteria.

Objective: In this study, we aimed to evaluate a prediction model for 30-day mortality after MRSA bacteremia infection diagnosis with reduced vancomycin susceptibility (MRSA-RVS) considering multiple computable phenotypes using EHR data.

Methods: We used EHR data from a large academic health center in the United States to replicate the original study conducted in Taiwan. We derived multiple computable phenotypes of risk factors and predictors used in the original study, reported stratified descriptive statistics, and assessed the performance of the prediction model.

Results: In our replication study, it was possible to (re)compute most of the original variables. Nevertheless, for certain variables, their computable phenotypes can only be approximated by proxy with structured EHR data items, especially the composite clinical indices such as the Pitt bacteremia score. Even computable phenotype for the outcome variable was subject to variation on the basis of the admission/discharge windows. The replicated prediction model exhibited only a mild discriminatory ability.

Conclusion: Despite the rich information in EHR data, replication of prediction models involving complex predictors is still challenging, often due to the limited availability of validated computable phenotypes. On the other hand, it is often possible to derive proxy computable phenotypes that can be further validated and calibrated.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.ijmedinf.2021.104531DOI Listing
September 2021

The role of sex and rurality in cancer fatalistic beliefs and cancer screening utilization in Florida.

Cancer Med 2021 Sep 13;10(17):6048-6057. Epub 2021 Jul 13.

Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA.

Background: People's fatalistic beliefs about cancer can influence their cancer prevention behaviors. We examined the association between fatalistic beliefs and breast and colorectal cancer screening among residents of north-central Florida and tested whether there exists any sex or rural-non-rural disparities in the association.

Methods: We conducted a cross-sectional, random digit dialing telephone survey of 895 adults residing in north-central Florida in 2017. Using weighted logistic models, we examined the association between (1) respondents' sociodemographic characteristics and cancer fatalistic beliefs and (2) cancer fatalistic beliefs and cancer screening utilization among screening eligible populations. We tested a series of sex and rurality by fatalistic belief interactions.

Results: Controlling for sociodemographics, we found the agreement with "It seems like everything causes cancer" was associated with a higher likelihood of having a mammogram (odds ratio [OR]: 3.34; 95% confidence interval [CI]: 1.17-9.51), while the agreement with "Cancer is most often caused by a person's behavior or lifestyle" was associated with a higher likelihood of having a blood stool test (OR: 1.85; 95% CI: 1.12-3.05) or a sigmoidoscopy or colonoscopy among women (OR: 2.65; 95% CI: 1.09-6.44). We did not observe any rural-non-rural disparity in the association between fatalistic beliefs and cancer screening utilization.

Conclusions: Some, but not all, cancer fatalistic beliefs are associated with getting breast and colorectal cancer screening in north-central Florida. Our study highlights the need for more research to better understand the social and cultural factors associated with cancer screening utilization.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/cam4.4122DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8419763PMC
September 2021

Targeting NF-κB c-Rel in regulatory T cells to treat corneal transplantation rejection.

Am J Transplant 2021 Jul 13. Epub 2021 Jul 13.

Qingdao Eye Hospital of Shandong First Medical University, Qingdao, China.

The relevance of Tregs in the induction of tolerance against corneal allografts has been well established. Although it is well known that the conversion of Tregs into effector-like cells contributes to the loss of corneal immune privilege, the underlying mechanism is still not fully understood. Using heterologous penetrating keratoplasty model, we found that Tregs from corneal allograft rejected mice (inflam-Tregs) exhibit impaired function and characteristics of effector T cells. Further study showed that the expression of NF-κB c-Rel, a key mediator of effector T cell function, was significantly increased in inflam-Tregs. Mechanistic study revealed that elevated NF-κB c-Rel level in inflam-Tregs impaired Treg function through the promotion of inflammatory cytokine production and glycolysis. More importantly, we demonstrated that targeting NF-κB c-Rel was able to improve the immune suppressive function of inflam-Tregs in vitro and enhance the potential of them to suppress corneal transplantation rejection. Therefore, our current study identified NF-κB c-Rel as a key mediator of the conversion of Tregs into effector-like cells when under inflammatory environment.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1111/ajt.16760DOI Listing
July 2021

Comparing the downstream costs and healthcare utilization associated with the use of low-dose computed tomography (LDCT) in lung cancer screening in patients with and without alzheimer's disease and related dementias (ADRD).

Curr Med Res Opin 2021 Jul 26:1-7. Epub 2021 Jul 26.

Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, FL, USA.

Objective: This study aims to compare the downstream costs and healthcare utilization associated with using low-dose computed tomography (LDCT) for lung cancer screening in patients with and without Alzheimer's disease and related dementias (ADRD).

Methods: Based on data from IBM MarketScan Commercial Claims Databases (2014-2018), we have identified four study cohorts: ADRD and non-ADRD patients who went through LDCT screening; ADRD and non-ADRD patients without LDCT screening. Annually healthcare utilization and cost were grouped into outpatient, inpatient, and pharmacy. We used difference-in-differences (DID) models to estimate the downstream healthcare utilization and cost associated with LDCT screening in both ADRD and non-ADRD population. We used a difference-in-difference-in-differences (DDD) model to explore whether LDCT screening was associated with higher downstream cost and healthcare utilization in ADRD population than non-ADRD population.

Result: Compared to individuals without LDCT screening, LDCT screening was associated with increased outpatient visits (2.1, 95% CI 0.7, 3.4) and outpatient cost ($2301.0, 95% CI 296.2, 4305.8) in the ADRD population and increased outpatient visits (0.6, 95% CI 0.1, 1.1) in the non-ADRD population within 1 year after screening. Compared with the non-ADRD population, LDCT screening was found to be associated with an additional 1.5 (95% CI 0.2, 2.8) outpatient visits, 0.7 (95% CI 0.1, 1.3) days of inpatient stays, and $4,960.4 (95% CI 532.7, 9388.0) in overall healthcare costs within 1-year after LDCT in the ADRD population (all  < .5).

Conclusion: The downstream cost and healthcare utilization associated with LDCT screening were found to be higher in the ADRD population compared to the average population.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1080/03007995.2021.1953972DOI Listing
July 2021

A varied approach to left ventricular assist device follow-up improves cost-effectiveness.

Curr Med Res Opin 2021 Sep 20;37(9):1501-1505. Epub 2021 Jul 20.

Department of Pharmaceutical Outcomes and Policy, University of Florida, Gainesville, FL, USA.

Background: Left ventricular assist device (LVAD) implantation improves outcomes in advanced heart failure, however, the optimal frequency of outpatient assessments to improve cost-effectiveness and potentially avert readmissions is unclear.

Methods: To test if varying the frequency of follow-up after LVAD implantation reduces readmissions and improves cost-effectiveness, a less intensive follow-up (LIFU) strategy with scheduled visits at 1 month and then every 6 months was compared to an intensive follow-up (IFU) group with scheduled visits at 1, 2, and 4 weeks, and then every 3 months post-implant. We developed a decision-tree model to evaluate the cost-effectiveness of different follow-up schedules at 3, 6, and 12-months. The readmission rates for LIFU and IFU, along with the associated costs, were estimated using data from the IBM MarketScan Commercial Claims Databases (2015-2018). A total of 349 patients were enrolled, with 193 and 156 in the IFU and LIFU groups.

Results: Patients with IFU were found to have a lower risk for readmission at 3 months (HR: 0.69, 95% confidence interval (CI): 0.60-0.79), but this difference diminished overtime at 6 months (HR: 0.84, 95% CI: 0.73-0.96) and 12 months (HR: 0.94, 95% CI: 0.83-1.06). The incremental net benefit of IFU, when compared with LIFU, is greatest in the first 3 months and also diminishes over time (3 months: $19616, 6 months $9257, 12 months $717).

Conclusions: An initial IFU strategy, followed by a period of de-escalation at the 6-month post-implant mark in lower-risk patients, may be a more cost-effective strategy to provide follow-up care while not predisposing patients to a higher risk of readmission.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1080/03007995.2021.1948395DOI Listing
September 2021

Human dermal fibroblasts support the development of human primordial/primary follicles in a 3-dimensional alginate matrix culture system.

Ann Transl Med 2021 May;9(10):868

Department of Obstetrics and Gynecology, Shanghai Everjoy Medical Polyclinic, Shanghai, China.

Background: Alginate matrix 3-dimensional culture offers the opportunity for the development and maturation of human secondary follicles in vitro. However, alginate may not be the most suitable culture system for human primordial/primary follicles . Thus, the innovation of alginate matrix 3-dimensional culture systems for human primordial/primary follicles could hold promise as an ideal approach to restoring fertility.

Methods: We extracted primordial/primary follicles from ovarian tissues collected from patients with non-ovarian benign gynecological conditions. Fibroblasts were isolated from dermal tissue from 1 male patient who had undergone posthectomy. The isolated human follicles were randomly divided into 2 groups and encapsulated within fibroblast-alginate-hydrogels or alginate hydrogels. The survival and growth of human primordial/primary follicles were measured after 21 days of culture.

Results: The dermal fibroblasts in alginate hydrogel microcapsules were round in shape, and were distributed as uniform clouds on the surface and gaps of the alginate. After 21 days of culture, the survival rate of follicles in the fibroblast-alginate group was higher than that of the alginate group (P<0.05). The diameter of follicles in the fibroblast-alginate group and the alginate group after 21 days of culture was 152.80±13.64 and 129.14±9.95 μm, respectively (P<0.05). After 21-day culture, the mean cpm (log-converted) for 3H-thymidine incorporated by granulosa cells in the fibroblast-alginate and alginate groups was 6.87±0.24 and 4.63±0.38, respectively (P<0.05). After 21 days of culture, the messenger RNA expression levels of growth differentiation factor 9 (GDF9) and bone morphogenetic protein 15 (BMP15) were significantly higher in oocytes in fibroblast-alginate hydrogels than in those in alginate hydrogels (P<0.05).

Conclusions: Human fibroblasts are beneficial to the development of human follicles in 3-dimensional culture alginate gel systems over a long period of time. More studies are required to investigate the molecular biological mechanisms of human fibroblasts that promote follicle growth .
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.21037/atm-21-2125DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8184424PMC
May 2021

The application of artificial intelligence and data integration in COVID-19 studies: a scoping review.

J Am Med Inform Assoc 2021 08;28(9):2050-2067

Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA.

Objective: To summarize how artificial intelligence (AI) is being applied in COVID-19 research and determine whether these AI applications integrated heterogenous data from different sources for modeling.

Materials And Methods: We searched 2 major COVID-19 literature databases, the National Institutes of Health's LitCovid and the World Health Organization's COVID-19 database on March 9, 2021. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline, 2 reviewers independently reviewed all the articles in 2 rounds of screening.

Results: In the 794 studies included in the final qualitative analysis, we identified 7 key COVID-19 research areas in which AI was applied, including disease forecasting, medical imaging-based diagnosis and prognosis, early detection and prognosis (non-imaging), drug repurposing and early drug discovery, social media data analysis, genomic, transcriptomic, and proteomic data analysis, and other COVID-19 research topics. We also found that there was a lack of heterogenous data integration in these AI applications.

Discussion: Risk factors relevant to COVID-19 outcomes exist in heterogeneous data sources, including electronic health records, surveillance systems, sociodemographic datasets, and many more. However, most AI applications in COVID-19 research adopted a single-sourced approach that could omit important risk factors and thus lead to biased algorithms. Integrating heterogeneous data for modeling will help realize the full potential of AI algorithms, improve precision, and reduce bias.

Conclusion: There is a lack of data integration in the AI applications in COVID-19 research and a need for a multilevel AI framework that supports the analysis of heterogeneous data from different sources.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/jamia/ocab098DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8344463PMC
August 2021

Examination of Early CNS Symptoms and Severe Coronavirus Disease 2019: A Multicenter Observational Case Series.

Crit Care Explor 2021 Jun 11;3(6):e0456. Epub 2021 Jun 11.

Department of Clinical and Health Psychology, University of Florida, Gainesville, FL.

To determine if early CNS symptoms are associated with severe coronavirus disease 2019.

Design: A retrospective, observational case series study design.

Setting: Electronic health records were reviewed for patients from five healthcare systems across the state of Florida, United States.

Patients: A clinical sample ( = 36,615) of patients with confirmed diagnosis of coronavirus disease 2019 were included. Twelve percent ( = 4,417) of the sample developed severe coronavirus disease 2019, defined as requiring critical care, mechanical ventilation, or diagnosis of acute respiratory distress syndrome, sepsis, or severe inflammatory response syndrome.

Interventions: None.

Measurement And Main Results: We reviewed the electronic health record for diagnosis of early CNS symptoms (encephalopathy, headache, ageusia, anosmia, dizziness, acute cerebrovascular disease) between 14 days before the diagnosis of coronavirus disease 2019 and 8 days after the diagnosis of coronavirus disease 2019, or before the date of severe coronavirus disease 2019 diagnosis, whichever came first. Hierarchal logistic regression models were used to examine the odds of developing severe coronavirus disease 2019 based on diagnosis of early CNS symptoms. Severe coronavirus disease 2019 patients were significantly more likely to have early CNS symptoms (32.8%) compared with nonsevere patients (6.11%; χ[1] = 3,266.08, < 0.0001, φ = 0.29). After adjusting for demographic variables and pertinent comorbidities, early CNS symptoms were significantly associated with severe coronavirus disease 2019 (odds ratio = 3.21). Diagnosis of encephalopathy (odds ratio = 14.38) was associated with greater odds of severe coronavirus disease 2019; whereas diagnosis of anosmia (odds ratio = 0.45), ageusia (odds ratio = 0.46), and headache (odds ratio = 0.63) were associated with reduced odds of severe coronavirus disease 2019.

Conclusions: Early CNS symptoms, and specifically encephalopathy, are differentially associated with risk of severe coronavirus disease 2019 and may serve as an early marker for differences in clinical disease course. Therapies for early coronavirus disease 2019 are scarce, and further identification of subgroups at risk may help to advance understanding of the severity trajectories and enable focused treatment.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1097/CCE.0000000000000456DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8202548PMC
June 2021

Evaluation of clustering and topic modeling methods over health-related tweets and emails.

Artif Intell Med 2021 07 7;117:102096. Epub 2021 May 7.

Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA.

Background: Internet provides different tools for communicating with patients, such as social media (e.g., Twitter) and email platforms. These platforms provided new data sources to shed lights on patient experiences with health care and improve our understanding of patient-provider communication. Several existing topic modeling and document clustering methods have been adapted to analyze these new free-text data automatically. However, both tweets and emails are often composed of short texts; and existing topic modeling and clustering approaches have suboptimal performance on these short texts. Moreover, research over health-related short texts using these methods has become difficult to reproduce and benchmark, partially due to the absence of a detailed comparison of state-of-the-art topic modeling and clustering methods on these short texts.

Methods: We trained eight state-of- the-art topic modeling and clustering algorithms on short texts from two health-related datasets (tweets and emails): Latent Semantic Indexing (LSI), Latent Dirichlet Allocation (LDA), LDA with Gibbs Sampling (GibbsLDA), Online LDA, Biterm Model (BTM), Online Twitter LDA, and Gibbs Sampling for Dirichlet Multinomial Mixture (GSDMM), as well as the k-means clustering algorithm with two different feature representations: TF-IDF and Doc2Vec. We used cluster validity indices to evaluate the performance of topic modeling and clustering: two internal indices (i.e. assessing the goodness of a clustering structure without external information) and five external indices (i.e. comparing the results of a cluster analysis to an externally known provided class labels).

Results: In overall, for number of clusters (k) from 2 to 50, Online Twitter LDA and GSDMM achieved the best performance in terms of internal indices, while LSI and k-means with TF-IDF had the highest external indices. Also, of all tweets (N = 286, 971; HPV represents 94.6% of tweets and lynch syndrome represents 5.4%), for k = 2, most of the methods could respect this initial clustering distribution. However, we found model performance varies with the source of data and hyper-parameters such as the number of topics and the number of iterations used to train the models. We also conducted an error analysis using the Hamming loss metric, for which the poorest value was obtained by GSDMM on both datasets.

Conclusions: Researchers hoping to group or classify health related short-text data can expect to select the most suitable topic modeling and clustering methods for their specific research questions. Therefore, we presented a comparison of the most common used topic modeling and clustering algorithms over two health-related, short-text datasets using both internal and external clustering validation indices. Internal indices suggested Online Twitter LDA and GSDMM as the best, while external indices suggested LSI and k-means with TF-IDF as the best. In summary, our work suggested researchers can improve their analysis of model performance by using a variety of metrics, since there is not a single best metric.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.artmed.2021.102096DOI Listing
July 2021

How the clinical research community responded to the COVID-19 pandemic: an analysis of the COVID-19 clinical studies in ClinicalTrials.gov.

JAMIA Open 2021 Apr 20;4(2):ooab032. Epub 2021 Apr 20.

Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, Florida, USA.

Objective: In the past few months, a large number of clinical studies on the novel coronavirus disease (COVID-19) have been initiated worldwide to find effective therapeutics, vaccines, and preventive strategies for COVID-19. In this study, we aim to understand the landscape of COVID-19 clinical research and identify the issues that may cause recruitment difficulty or reduce study generalizability.

Methods: We analyzed 3765 COVID-19 studies registered in the largest public registry-ClinicalTrials.gov, leveraging natural language processing (NLP) and using descriptive, association, and clustering analyses. We first characterized COVID-19 studies by study features such as phase and tested intervention. We then took a deep dive and analyzed their eligibility criteria to understand whether these studies: (1) considered the reported underlying health conditions that may lead to severe illnesses, and (2) excluded older adults, either explicitly or implicitly, which may reduce the generalizability of these studies to the older adults population.

Results: Our analysis included 2295 interventional studies and 1470 observational studies. Most trials did not explicitly exclude older adults with common chronic conditions. However, known risk factors such as diabetes and hypertension were considered by less than 5% of trials based on their trial description. Pregnant women were excluded by 34.9% of the studies.

Conclusions: Most COVID-19 clinical studies included both genders and older adults. However, risk factors such as diabetes, hypertension, and pregnancy were under-represented, likely skewing the population that was sampled. A careful examination of existing COVID-19 studies can inform future COVID-19 trial design towards balanced internal validity and generalizability.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/jamiaopen/ooab032DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8083215PMC
April 2021

Discovery of Novel Benzothiazepinones as Irreversible Covalent Glycogen Synthase Kinase 3β Inhibitors for the Treatment of Acute Promyelocytic Leukemia.

J Med Chem 2021 06 24;64(11):7341-7358. Epub 2021 May 24.

Department of Medicinal Chemistry, School of Pharmacy, Fudan University, Shanghai 201203, China.

Recently, irreversible inhibitors have attracted great interest in antitumors due to their advantages of forming covalent bonds to target proteins. Herein, some benzothiazepinone compounds (BTZs) have been designed and synthesized as novel covalent GSK-3β inhibitors with high selectivity for the kinase panel. The irreversible covalent binding mode was identified by kinetics and mass spectrometry, and the main labeled residue was confirmed to be the unique Cys14 that exists only in GSK-3β. The candidate - (IC = 6.6 μM) showed good proliferation inhibition and apoptosis-inducing ability to leukemia cell lines, low cytotoxicity on normal cell lines, and no hERG inhibition, which hinted the potential efficacy and safety. Furthermore, - exhibited decent pharmacokinetic properties and remarkably inhibited tumor growth in the acute promyelocytic leukemia (APL) mouse model. All the results suggest that these newly irreversible BTZ compounds might be useful in the treatment of cancer such as APL.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1021/acs.jmedchem.0c02254DOI Listing
June 2021

Exploring the feasibility of using real-world data from a large clinical data research network to simulate clinical trials of Alzheimer's disease.

NPJ Digit Med 2021 May 14;4(1):84. Epub 2021 May 14.

Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, FL, USA.

In this study, we explored the feasibility of using real-world data (RWD) from a large clinical research network to simulate real-world clinical trials of Alzheimer's disease (AD). The target trial (i.e., NCT00478205) is a Phase III double-blind, parallel-group trial that compared the 23 mg donepezil sustained release with the 10 mg donepezil immediate release formulation in patients with moderate to severe AD. We followed the target trial's study protocol to identify the study population, treatment regimen assignments and outcome assessments, and to set up a number of different simulation scenarios and parameters. We considered two main scenarios: (1) a one-arm simulation: simulating a standard-of-care (SOC) arm that can serve as an external control arm; and (2) a two-arm simulation: simulating both intervention and control arms with proper patient matching algorithms for comparative effectiveness analysis. In the two-arm simulation scenario, we used propensity score matching controlling for baseline characteristics to simulate the randomization process. In the two-arm simulation, higher serious adverse event (SAE) rates were observed in the simulated trials than the rates reported in original trial, and a higher SAE rate was observed in the 23 mg arm than in the 10 mg SOC arm. In the one-arm simulation scenario, similar estimates of SAE rates were observed when proportional sampling was used to control demographic variables. In conclusion, trial simulation using RWD is feasible in this example of AD trial in terms of safety evaluation. Trial simulation using RWD could be a valuable tool for post-market comparative effectiveness studies and for informing future trials' design. Nevertheless, such an approach may be limited, for example, by the availability of RWD that matches the target trials of interest, and further investigations are warranted.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41746-021-00452-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8121837PMC
May 2021

The association between cognitive impairment and breast and colorectal cancer screening utilization.

BMC Cancer 2021 May 12;21(1):539. Epub 2021 May 12.

Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, 2004 Mowry Road, PO Box 100177, Gainesville, FL, 32610, USA.

Background: Undergoing cancer screening is a debatable topic in patients with cognitive impairment. In this study, we aimed to examine the utilization and predictors of breast and colorectal cancer screening among screening eligible, cognitively impaired individuals.

Methods: We analyzed the 2018 and 2019 National Health Interview Survey data (n = 12,965 and 24,782, respectively) on individuals eligible for breast or colorectal cancer screening. We calculated the percentage of cancer screening eligible individuals who received mammogram or colonoscopy by cognitive impairment status. We used multivariable logistic regression to examine whether having a recent mammogram or colonoscopy differed by cognitive impairment status, adjusting for covariates.

Results: We observed a significantly lower percentage of mammogram use in the screening eligible, cognitively impaired (mild or severe) versus unimpaired women. Adjusting for the covariates, the cognitively impaired women, mild (odds ratio [OR] = 0.85; p = 0.015) or severe (OR = 0.54; p <  0.001), were less likely to have had a recent mammogram compared to the cognitively unimpaired women. Although statistically non-significant, the percentage of colonoscopy use in the screening eligible, cognitively impaired individuals were slightly higher than that in the cognitively unimpaired individuals. In the regression analysis, we found the cognitively impaired men, mild (OR = 0.79; p <  0.001) or severe (OR = 0.69; p = 0.038), were less likely to have had a recent colonoscopy compared to the cognitively unimpaired men. More studies are needed to examine the multilevel factors that underpin the difference in cancer screening utilization in this vulnerable population.

Conclusion: Our results highlight the need for additional research to address utilization and effectiveness of cancer screening in individuals with cognitive impairment.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12885-021-08321-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8114528PMC
May 2021

Planning for patient-reported outcome implementation: Development of decision tools and practical experience across four clinics.

J Clin Transl Sci 2020 Apr 6;4(6):498-507. Epub 2020 Apr 6.

Northwestern University, Chicago, IL, USA.

Introduction: Many institutions are attempting to implement patient-reported outcome (PRO) measures. Because PROs often change clinical workflows significantly for patients and providers, implementation choices can have major impact. While various implementation guides exist, a stepwise list of decision points covering the full implementation process and drawing explicitly on a sociotechnical conceptual framework does not exist.

Methods: To facilitate real-world implementation of PROs in electronic health records (EHRs) for use in clinical practice, members of the EHR Access to Seamless Integration of Patient-Reported Outcomes Measurement Information System (PROMIS) Consortium developed structured PRO implementation planning tools. Each institution pilot tested the tools. Joint meetings led to the identification of critical sociotechnical success factors.

Results: Three tools were developed and tested: (1) a summarizes the empirical knowledge and guidance about PRO implementation in routine clinical care; (2) a allows decision tracking; and (3) an simplifies creation of a sharable implementation plan. Seven lessons learned during implementation underscore the iterative nature of planning and the importance of the clinician champion, as well as the need to understand aims, manage implementation barriers, minimize disruption, provide ample discussion time, and continuously engage key stakeholders.

Conclusions: Highly structured planning tools, informed by a sociotechnical perspective, enabled the construction of clear, clinic-specific plans. By developing and testing three reusable tools (freely available for immediate use), our project addressed the need for consolidated guidance and created new materials for PRO implementation planning. We identified seven important lessons that, while common to technology implementation, are especially critical in PRO implementation.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1017/cts.2020.37DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8057386PMC
April 2020

Identifying Clinical Risk Factors for Opioid Use Disorder using a Distributed Algorithm to Combine Real-World Data from a Large Clinical Data Research Network.

AMIA Annu Symp Proc 2020 25;2020:1220-1229. Epub 2021 Jan 25.

Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, The University of Pennsylvania, Philadelphia, PA, USA.

Because they contain detailed individual-level data on various patient characteristics including their medical conditions and treatment histories, electronic health record (EHR) systems have been widely adopted as an efficient source for health research. Compared to data from a single health system, real-world data (RWD) from multiple clinical sites provide a larger and more generalizable population for accurate estimation, leading to better decision making for health care. However, due to concerns over protecting patient privacy, it is challenging to share individual patient-level data across sites in practice. To tackle this issue, many distributed algorithms have been developed to transfer summary-level statistics to derive accurate estimates. Nevertheless, many of these algorithms require multiple rounds of communication to exchange intermediate results across different sites. Among them, the One-shot Distributed Algorithm for Logistic regression (termed ODAL) was developed to reduce communication overhead while protecting patient privacy. In this paper, we applied the ODAL algorithm to RWD from a large clinical data research network-the OneFlorida Clinical Research Consortium and estimated the associations between risk factors and the diagnosis of opioid use disorder (OUD) among individuals who received at least one opioid prescription. The ODAL algorithm provided consistent findings of the associated risk factors and yielded better estimates than meta-analysis.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8075517PMC
June 2021

Using Real-World Data to Rationalize Clinical Trials Eligibility Criteria Design: A Case Study of Alzheimer's Disease Trials.

AMIA Annu Symp Proc 2020 25;2020:717-726. Epub 2021 Jan 25.

University of Florida, Gainesville, Florida, USA.

Low trial generalizability is a concern. The Food and Drug Administration had guidance on broadening trial eligibility criteria to enroll underrepresented populations. However, investigators are hesitant to do so because of concerns over patient safety. There is a lack of methods to rationalize criteria design. In this study, we used data from a large research network to assess how adjustments of eligibility criteria can jointly affect generalizability and patient safety (i.e the number of serious adverse events [SAEs]). We first built a model to predict the number of SAEs. Then, leveraging an a priori generalizability assessment algorithm, we assessed the changes in the number of predicted SAEs and the generalizability score, simulating the process of dropping exclusion criteria and increasing the upper limit of continuous eligibility criteria. We argued that broadening of eligibility criteria should balance between potential increases of SAEs and generalizability using donepezil trials as a case study.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8075542PMC
June 2021

Developing and Validating a Computable Phenotype for the Identification of Transgender and Gender Nonconforming Individuals and Subgroups.

AMIA Annu Symp Proc 2020 25;2020:514-523. Epub 2021 Jan 25.

University of Florida, Gainesville, Florida, USA.

Transgender and gender nonconforming (TGNC) individuals face significant marginalization, stigma, and discrimination. Under-reporting of TGNC individuals is common since they are often unwilling to self-identify. Meanwhile, the rapid adoption of electronic health record (EHR) systems has made large-scale, longitudinal real-world clinical data available to research and provided a unique opportunity to identify TGNC individuals using their EHRs, contributing to a promising routine health surveillance approach. Built upon existing work, we developed and validated a computable phenotype (CP) algorithm for identifying TGNC individuals and their natal sex (i.e., male-to-female or female-to-male) using both structured EHR data and unstructured clinical notes. Our CP algorithm achieved a 0.955 F1-score on the training data and a perfect F1-score on the independent testing data. Consistent with the literature, we observed an increasing percentage of TGNC individuals and a disproportionate burden of adverse health outcomes, especially sexually transmitted infections and mental health distress, in this population.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8075543PMC
June 2021

Leverage Real-world Longitudinal Data in Large Clinical Research Networks for Alzheimer's Disease and Related Dementia (ADRD).

AMIA Annu Symp Proc 2020 25;2020:393-401. Epub 2021 Jan 25.

Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, The University of Pennsylvania, Philadelphia, PA, USA.

With vast amounts ofpatients' medical information, electronic health records (EHRs) are becoming one of the most important data sources in biomedical and health care research. Effectively integrating data from multiple clinical sites can help provide more generalized real-world evidence that is clinically meaningful. To analyze the clinical data from multiple sites, distributed algorithms are developed to protect patient privacy without sharing individual-level medical information. In this paper, we applied the One-shot Distributed Algorithm for Cox proportional hazard model (ODAC) to the longitudinal data from the OneFlorida Clinical Research Consortium to demonstrate the feasibility of implementing the distributed algorithms in large research networks. We studied the associations between the clinical risk factors and Alzheimer's disease and related dementia (ADRD) onsets to advance clinical research on our understanding of the complex risk factors of ADRD and ultimately improve the care of ADRD patients.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8075520PMC
June 2021

Semantic standards of external exposome data.

Environ Res 2021 06 24;197:111185. Epub 2021 Apr 24.

Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA; Cancer Informatics Shared Resource, University of Florida Health Cancer Center, Gainesville, FL, USA. Electronic address:

An individual's health and conditions are associated with a complex interplay between the individual's genetics and his or her exposures to both internal and external environments. Much attention has been placed on characterizing of the genome in the past; nevertheless, genetics only account for about 10% of an individual's health conditions, while the remaining appears to be determined by environmental factors and gene-environment interactions. To comprehensively understand the causes of diseases and prevent them, environmental exposures, especially the external exposome, need to be systematically explored. However, the heterogeneity of the external exposome data sources (e.g., same exposure variables using different nomenclature in different data sources, or vice versa, two variables have the same or similar name but measure different exposures in reality) increases the difficulty of analyzing and understanding the associations between environmental exposures and health outcomes. To solve the issue, the development of semantic standards using an ontology-driven approach is inevitable because ontologies can (1) provide a unambiguous and consistent understanding of the variables in heterogeneous data sources, and (2) explicitly express and model the context of the variables and relationships between those variables. We conducted a review of existing ontology for the external exposome and found only four relevant ontologies. Further, the four existing ontologies are limited: they (1) often ignored the spatiotemporal characteristics of external exposome data, and (2) were developed in isolation from other conceptual frameworks (e.g., the socioecological model and the social determinants of health). Moving forward, the combination of multi-domain and multi-scale data (i.e., genome, phenome and exposome at different granularity) and different conceptual frameworks is the basis of health outcomes research in the future.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.envres.2021.111185DOI Listing
June 2021

Oral cancer knowledge and screening behavior among smokers and non-smokers in rural communities.

BMC Cancer 2021 Apr 20;21(1):430. Epub 2021 Apr 20.

Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, PO Box 100177, 2004 Mowry Road, Suite 2251, Gainesville, FL, 32610-0177, USA.

Background: Research suggests having an oral and pharyngeal cancer (OPC) examination for early diagnosis can increase survival rate. However, the OPC screening rate is low in certain populations. To improve OPC screening rate, this study identified factors that are associated with having an OPC examination.

Methods: Participants with landlines and aged 25 years and older were recruited from six northern Florida counties. Bivariate and logistic regressions were used to predict the outcome of whether the participants had ever had an OPC examination as well as whether participants had ever heard of an OPC examination.

Results: Of 2260 participants with a mean age of 55.9 ± 15.0 years, the majority of participants never smoked (53.4%), self-identified as Whites (70.6%), and had some college or 2-year degree education (30.3%). Smokers were significantly less likely to have ever heard of an OPC examination than those who never smoked. Significant interaction between smoking status and race, and smoking status and social support interaction were found. Whites who never smoked were more likely to have had an OPC examination than non-Whites who never smoked. Former and current smokers with greater social support were more likely to have had an OPC examination than those with lower social support.

Conclusion: The findings from this study inform the need to enhance the awareness of having an OPC examination among smokers and to reduce barriers for racial minority populations to receive an OPC examination. Future research is warranted to develop interventions to target certain populations to improve the rate of OPC examination.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12885-021-08198-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8056680PMC
April 2021

MTM1 plays an important role in the regulation of zinc tolerance in Saccharomyces cerevisiae.

J Trace Elem Med Biol 2021 Jul 14;66:126759. Epub 2021 Apr 14.

College of Life Sciences, Shanghai Normal University, Shanghai, 200234, China. Electronic address:

Background: Acquisition and distribution of zinc supports a number of biological processes. Various molecular factors are involved in zinc metabolism but not fully explored.

Basic Procedures: Spontaneous mutants were generated in yeast with excess zinc culture followed by whole genome DNA sequencing to discover zinc metabolism related genes by bioinformatics. An identified mutant was characterized through metallomic and molecular biology methods.

Main Findings: Here we reported that MTM1 knockout cells displayed much stronger zinc tolerance than wild type cells on SC medium when exposed to excess zinc. Zn accumulation of mtm1Δ cells was dramatically decreased compared to wild type cells under excessive zinc condition due to MTM1 deletion reduced zinc uptake. ZRC1 mRNA level of mtm1Δ cells was significantly higher than that in the wild-type strain leading to increased vacuolar zinc accumulations in mtm1Δ cells. The mRNA levels of ZRT1 and ZAP1 decreased in mtm1Δ cells contributing to less Zn uptake. The zrc1Δmtm1Δ double knockout strain exhibited Zn sensitivity. MTM1 knockout did not afford resistance to excess zinc through an effect mediated through an influence on levels of ROS. Superoxide dismutase 2 (Sod2p) activity in mtm1Δ cells was severely impaired and not restored through Zn supplementation. Meanwhile, additional Zn showed no significant effect on the localization and expression of Mtm1p.

Principal Conclusions: Our study reveals the MTM1 gene plays an important role in the regulation of zinc homeostasis in yeast cells via changing zinc uptake and distribution. This discovery provides new insights for better understanding biochemical communication between vacuole and mitochondrial in relation to zinc-metabolism.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jtemb.2021.126759DOI Listing
July 2021

When text simplification is not enough: could a graph-based visualization facilitate consumers' comprehension of dietary supplement information?

JAMIA Open 2021 Jan 4;4(1):ooab026. Epub 2021 Apr 4.

Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, Florida, USA.

Objective: Dietary supplements are widely used. However, dietary supplements are not always safe. For example, an estimated 23 000 emergency room visits every year in the United States were attributed to adverse events related to dietary supplement use. With the rapid development of the Internet, consumers usually seek health information including dietary supplement information online. To help consumers access quality online dietary supplement information, we have identified trustworthy dietary supplement information sources and built an evidence-based knowledge base of dietary supplement information-the integrated DIetary Supplement Knowledge base (iDISK) that integrates and standardizes dietary supplement related information across these different sources. However, as information in iDISK was collected from scientific sources, the complex medical jargon is a barrier for consumers' comprehension. The objective of this study is to assess how different approaches to simplify and represent dietary supplement information from iDISK will affect lay consumers' comprehension.

Materials And Methods: Using a crowdsourcing platform, we recruited participants to read dietary supplement information in 4 different representations from iDISK: (1) original text, (2) syntactic and lexical text simplification (TS), (3) manual TS, and (4) a graph-based visualization. We then assessed how the different simplification and representation strategies affected consumers' comprehension of dietary supplement information in terms of accuracy and response time to a set of comprehension questions.

Results: With responses from 690 qualified participants, our experiments confirmed that the manual approach, as expected, had the best performance for both accuracy and response time to the comprehension questions, while the graph-based approach ranked the second outperforming other representations. In some cases, the graph-based representation outperformed the manual approach in terms of response time.

Conclusions: A hybrid approach that combines text and graph-based representations might be needed to accommodate consumers' different information needs and information seeking behavior.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/jamiaopen/ooab026DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8029346PMC
January 2021

A Natural Language Processing Tool to Extract Quantitative Smoking Status from Clinical Narratives.

IEEE Int Conf Healthc Inform 2020 Nov-Dec;2020. Epub 2021 Mar 12.

Health Outcomes and Biomedical Informatics College of Medicine, University of Florida Gainesville, USA.

This study presents a natural language processing (NLP) tool to extract quantitative smoking information (e.g., Pack-Year, Quit Year, Smoking Year, and Pack per Day) from clinical notes and standardized them into Pack-Year unit. We annotated a corpus of 200 clinical notes from patients who had low-dose CT imaging procedures for lung cancer screening and developed an NLP system using a two-layer rule-engine structure. We divided the 200 notes into a training set and a test set and developed the NLP system only using the training set. The experimental results on the test set showed that our NLP system achieved the best F1 scores of 0.963 and 0.946 for lenient and strict evaluation, respectively.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1109/ICHI48887.2020.9374369DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8006894PMC
March 2021

Unraveling COVID-19: a large-scale characterization of 4.5 million COVID-19 cases using CHARYBDIS.

Res Sq 2021 Mar 1. Epub 2021 Mar 1.

Routinely collected real world data (RWD) have great utility in aiding the novel coronavirus disease (COVID-19) pandemic response [1,2]. Here we present the international Observational Health Data Sciences and Informatics (OHDSI) [3] Characterizing Health Associated Risks, and Your Baseline Disease In SARS-COV-2 (CHARYBDIS) framework for standardisation and analysis of COVID-19 RWD. We conducted a descriptive cohort study using a federated network of data partners in the United States, Europe (the Netherlands, Spain, the UK, Germany, France and Italy) and Asia (South Korea and China). The study protocol and analytical package were released on 11 June 2020 and are iteratively updated via GitHub [4]. We identified three non-mutually exclusive cohorts of 4,537,153 individuals with a clinical 886,193 , and 113,627 . All comorbidities, symptoms, medications, and outcomes are described by cohort in aggregate counts, and are available in an interactive website: https://data.ohdsi.org/Covid19CharacterizationCharybdis/. CHARYBDIS findings provide benchmarks that contribute to our understanding of COVID-19 progression, management and evolution over time. This can enable timely assessment of real-world outcomes of preventative and therapeutic options as they are introduced in clinical practice.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.21203/rs.3.rs-279400/v1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7941629PMC
March 2021

RACK1 mediates the advanced glycation end product-induced degradation of HIF-1α in nucleus pulposus cells via competing with HSP90 for HIF-1α binding.

Cell Biol Int 2021 Jun 3;45(6):1316-1326. Epub 2021 Mar 3.

Department of Orthopaedics, The First Affiliated Hospital of Soochow University, Suzhou, China.

Hyperglycemia can drive advanced glycation end product (AGE) accumulation and associated nucleus pulposus cell (NPC) dysfunction, but the basis for this activity has not been elucidated. Hypoxia-inducible factor-1α (HIF-1α) is subject to cell-type-specific AGE-mediated regulation. In the current study, we assessed the mechanistic relationship between AGE accumulation and HIF-1α degradation in NPCs. Immunohistochemical staining of degenerated nucleus pulposus (NP) samples was used to assess AGE levels. AGE impact on NPC survival and glycolysis-related gene expression was assessed via 3-(4,5)-dimethylthiazol(-z-y1)-3,5-di-phenyltetrazolium bromide assay and quantitative reverse-transcription polymerase chain reaction (qRT-PCR), while HIF-1α expression in NPCs following AGE treatment was monitored via Western blot analysis and qRT-PCR. Additionally, a luciferase reporter assay was used to monitor HIF-1α transcriptional activity. The importance of the receptor for activated C-kinase 1 (RACK1) as a mediator of HIF-1α degradation was evaluated through gain- and loss-of-function experiments. Competitive binding of RACK1 and HSP90 to HIF-1α was evaluated via immunoprecipitation. Increased AGE accumulation was evident in NP samples from diabetic patients, and AGE treatment resulted in reduced HIF-1α protein levels in NPCs that coincided with reduced HIF-1α transcriptional activity. AGE treatment impaired the stability of HIF-1α, leading to its RACK1-mediated proteasomal degradation in a manner independent of the canonical PHD-mediated degradation pathway. Additionally, RACK1 competed with HSP90 for HIF-1α binding following AGE treatment. AGE treatment of NPCs leads to HIF-1α protein degradation. RACK1 competes with HSP90 for HIF-1α binding following AGE treatment, resulting in posttranslational HIF-1α degradation. These results suggest that AGE is an intervertebral disc degeneration risk factor, and highlight potential avenues for the treatment or prevention of this disease.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/cbin.11574DOI Listing
June 2021
-->