[Application of aldosterone/direct renin ratio before drug washout in the screening of primary aldosteronism]

Objective: To explore the cut-off point of aldosterone/direct renin ratio (ADRR) before drug washout in the screening for primary aldosteronism (PA) in the Chinese population and reduce the potential risk caused by drug washout during PA screening. Methods: Hospitalized hypertensive patients in the Hypertension Ward of Fuwai Hospital, Chinese Academy of Medical Sciences from January 2017 to October 2019 were enrolled. PA was diagnosed according to the criterion of 2016 American Guideline and 2016 Chinese Consensus for PA. The plasma aldosterone concentration (PAC), direct renin concentration (DRC) and ADRR before and after drug washout were measured. The receiver operating characteristic (ROC) curve of ADRR was drawn and the maximal Youden index was used to determine the best cut-off value. Results: A total of 542 hypertensive patients were included, with 467 patients diagnosed with essential hypertension (EHT) (297 males and 170 females), and 75 patients diagnosed with PA (51 males and 24 females). Patients with PA had higher PAC and ADRR before and after drug washout than those with EHT(150.0 (130.0, 210.0) vs 120.0 (80.0, 170.0) ng/L, 170.0 (120.0, 260.0) vs 130.0 (90.0, 180.0) ng/L; 28.9 (15.9, 63.5) vs 4.3 (1.9, 11.8) (ng/L) / (mU/L) , 55.6 (39.0, 109.0) vs 9.8 (4.5, 21.3) (ng/L) /(mU/L), all P<0.001). However, DRC of PA patients before and after washout were lower than those with EHT (4.0 (2.0, 10.0) vs 27.0 (10.0, 64.0) mU/L, 3.0 (2.0, 4.0) vs 12.2 (5.0, 27.0) mU/L, P<0.001). In EHT and PA groups, PAC and ADRR significantly increased (P=0.001, P<0.001) , but DRC significantly decreased after drug washout (all P<0.001) . The area under the ROC curve of ADRR before drug washout was 0.868 (95%CI 0.836-0.895) with the best cut-off value of 7.8 (ng/L) / (mU/L) for the screening of PA .The sensitivity and specificity was 94.7% and 66.8%, respectively, with the maximal Youden index of 0.615. Conclusion: ADRR before drug washout > 7.8 (ng/L) / (mU/L) can be used as an alternative cut-off point to screen PA when drug washout is not available.

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

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.

NOTE

Accepted as a presentation at the 2020 IEEE International Conference on Healthcare Informatics (ICHI) Workshop on Health Natural Language Processing (HealthNLP 2020). https://ohnlp.github.io/HealthNLP2020/healthnlp2020# .

Use of Deep Learning to Analyze Social Media Discussions About the Human Papillomavirus Vaccine

IMPORTANCE

Human papillomavirus (HPV) vaccine hesitancy or refusal is common among parents of adolescents. An understanding of public perceptions from the perspective of behavior change theories can facilitate effective and targeted vaccine promotion strategies.

OBJECTIVE

To develop and validate deep learning models for understanding public perceptions of HPV vaccines from the perspective of behavior change theories using data from social media.

DESIGN, SETTING, AND PARTICIPANTS

This retrospective cohort study, conducted from April to August 2019, included longitudinal and geographic analyses of public perceptions regarding HPV vaccines, using sampled HPV vaccine-related Twitter discussions collected from January 2014 to October 2018.

MAIN OUTCOMES AND MEASURES

The prevalence of social media discussions related to the construct of health belief model (HBM) and theory of planned behavior (TPB), categorized by deep learning algorithms. Locally estimated scatterplot smoothing (LOESS) revealed trends of constructs. Social media users' US state-level home location information was extracted from their profiles, and geographic analyses were performed to identify the clustering of public perceptions of the HPV vaccine.

RESULTS

A total of 1 431 463 English-language posts from 486 116 unique usernames were collected. Deep learning algorithms achieved F-1 scores ranging from 0.6805 (95% CI, 0.6516-0.7094) to 0.9421 (95% CI, 0.9380-0.9462) in mapping discussions to the constructs of behavior change theories. LOESS revealed trends in constructs; for example, prevalence of perceived barriers, a construct of HBM, deceased from its apex in July 2015 (56.2%) to its lowest prevalence in October 2018 (28.4%; difference, 27.8%; P < .001); Positive attitudes toward the HPV vaccine, a construct of TPB, increased from early 2017 (30.7%) to 41.9% at the end of the study (difference, 11.2%; P < .001), while negative attitudes decreased from 42.3% to 31.3% (difference, 11.0%; P < .001) during the same period. Interstate variations in public perceptions of the HPV vaccine were also identified; for example, the states of Ohio and Maine showed a relatively high prevalence of perceived barriers (11 531 of 17 106 [67.4%] and 1157 of 1684 [68.7%]) and negative attitudes (9655 of 17 197 [56.1%] and 1080 of 1793 [60.2%]).

CONCLUSIONS AND RELEVANCE

This cohort study provided a good understanding of public perceptions on social media and evolving trends in terms of multiple dimensions. The interstate variations of public perceptions could be associated with the rise of local antivaccine sentiment. The methods described in this study represent an early contribution to using existing empirically and theoretically based frameworks that describe human decision-making in conjunction with more intelligent deep learning algorithms. Furthermore, these data demonstrate the ability to collect large-scale HPV vaccine perception and intention data that can inform public health communication and education programs designed to improve immunization rates at the community, state, or even national level.

MicroRNA predicts cognitive performance in healthy older adults

The expression of microRNA (miRNA) is influenced by ongoing biological processes, including aging, and has begun to play a role in the measurement of neurodegenerative processes in central nervous system. The purpose of this study is to utilize machine learning approaches to determine whether miRNA can be utilized as a blood-based biomarker of cognitive aging. A random forest regression combining miRNA with biological (brain volume), clinical (comorbid conditions), and demographic variables in 115 typically aging older adults explained the greatest level of variance in cognitive performance compared to the other machine learning models explored. Three miRNA (miR-140-5p, miR-197-3p, and miR-501-3p) were top-ranked predictors of multiple cognitive outcomes (Fluid, Crystallized, and Overall Cognition) and past studies of these miRNA link them to cellular senescence, inflammatory signals for atherosclerotic formation, and potential development of neurodegenerative disorders (e.g., Alzheimer's disease). Several novel miRNAs were also linked to age and multiple cognitive functions, findings which together warrant further exploration linking these miRNAs to brain-derived metrics of neurodegeneration in typically aging older adults.

Exploring Eating Disorder Topics on Twitter: Machine Learning Approach

Background

Eating disorders (EDs) are a group of mental illnesses that have an adverse effect on both mental and physical health. As social media platforms (eg, Twitter) have become an important data source for public health research, some studies have qualitatively explored the ways in which EDs are discussed on these platforms. Initial results suggest that such research offers a promising method for further understanding this group of diseases. Nevertheless, an efficient computational method is needed to further identify and analyze tweets relevant to EDs on a larger scale.

Objective

This study aims to develop and validate a machine learning–based classifier to identify tweets related to EDs and to explore factors (ie, topics) related to EDs using a topic modeling method.

Methods

We collected potential ED-relevant tweets using keywords from previous studies and annotated these tweets into different groups (ie, ED relevant vs irrelevant and then promotional information vs laypeople discussion). Several supervised machine learning methods, such as convolutional neural network (CNN), long short-term memory (LSTM), support vector machine, and naïve Bayes, were developed and evaluated using annotated data. We used the classifier with the best performance to identify ED-relevant tweets and applied a topic modeling method—Correlation Explanation (CorEx)—to analyze the content of the identified tweets. To validate these machine learning results, we also collected a cohort of ED-relevant tweets on the basis of manually curated rules.

Results

A total of 123,977 tweets were collected during the set period. We randomly annotated 2219 tweets for developing the machine learning classifiers. We developed a CNN-LSTM classifier to identify ED-relevant tweets published by laypeople in 2 steps: first relevant versus irrelevant (F₁ score=0.89) and then promotional versus published by laypeople (F₁ score=0.90). A total of 40,790 ED-relevant tweets were identified using the CNN-LSTM classifier. We also identified another set of tweets (ie, 17,632 ED-relevant and 83,557 ED-irrelevant tweets) posted by laypeople using manually specified rules. Using CorEx on all ED-relevant tweets, the topic model identified 162 topics. Overall, the coherence rate for topic modeling was 77.07% (1264/1640), indicating a high quality of the produced topics. The topics were further reviewed and analyzed by a domain expert.

Conclusions

A developed CNN-LSTM classifier could improve the efficiency of identifying ED-relevant tweets compared with the traditional manual-based method. The CorEx topic model was applied on the tweets identified by the machine learning–based classifier and the traditional manual approach separately. Highly overlapping topics were observed between the 2 cohorts of tweets. The produced topics were further reviewed by a domain expert. Some of the topics identified by the potential ED tweets may provide new avenues for understanding this serious set of disorders.

Role of BMAL1 and CLOCK in regulating the secretion of melatonin in chick retina under monochromatic green light

As the circadian pacemaker of birds, the retina possesses the ability to receive light information, generate circadian oscillation, and secrete melatonin. Previous studies have confirmed that monochromatic green light can accelerate the circadian rhythmic expression of clock genes in the chick retina, thereby increasing cAanat mRNA level and melatonin secretion. However, as the core components of the transcriptional-translational negative feedback loop, the role that cBmal1 and cClock plays in the regulation of the retinal molecular clock system and melatonin secretion under monochromatic green light is unknown. To explore their in these processes, embryonic chick retinal cells at six embryo ages were isolated and cultured under light-dark (LD) 12:12 monochromatic green light with, and the role of cBmal1 and cClock in the regulation of the retinal molecular clock and melatonin secretion in the chick retina was explored by siRNA interference and overexpression. The results showed siRNA interference and overexpression of cBmal1 obliterated the circadian rhythm of cCry1, cPer2, cPer3, cAanat, and melatonin secretion. Moreover, the siRNA interference of cBmal1 significantly reduced the average expression levels of the positive clock genes cBmal2 and cClock, positive clock protein CLOCK, negative clock genes cCry1, cCry2, cPer2, cPer3, as well as cAanat and retinal melatonin. The over-expression of cBmal1 increased the average levels of the above-detected targets. However, siRNA interference and overexpression of cClock did not change the rhythm of all of the clock genes, clock proteins, cAanat, and melatonin secretion, while it only affected the circadian mesors (24 h time series means), amplitudes, and acrophases (peak times) of cCry1, cPer2, cPer3, cAanat, and melatonin, as well as the average levels of arrhythmic cBmal2 and cCry2. Moreover, interference and overexpression of cClock did not affect cBmal1 mRNA level and BMAL1 protein expression. The above results reveal interference and overexpression of cBmal1 completely abolished the molecular circadian oscillation and the rhythm of melatonin output signal of chick retinal cells, indicating that cBmal1 is on the top of the avian retinal molecular clock feedback loop and regulates the downstream molecular clock oscillation and output under monochromatic green light. cClock plays a subordinate role in maintaining the circadian oscillation of the molecular clock and melatonin secretion in retinal cells, and it has a stabilizing and amplifying effect on molecular clock oscillation.

Dracocephalum heterophyllum (DH) Exhibits Potent Anti-Proliferative Effects on Autoreactive CD4+ T Cells and Ameliorates the Development of Experimental Autoimmune Uveitis

Experimental autoimmune uveitis (EAU) is a CD4⁺ T cell–mediated organ-specific autoimmune disease and has been considered as a model of human autoimmune uveitis. Dracocephalum heterophyllum (DH) is a Chinese herbal medicine used in treating hepatitis. DH suppressed the production of inflammatory cytokines through the recruitment of myeloid-derived suppressor cells (MDSCs) to the liver. However, it remains elusive whether DH can directly regulate CD4⁺ T cell biology and hence ameliorates the development of CD4⁺ T cell–mediated autoimmune disease. In the current study, we found that DH extract significantly suppressed the production of pro-inflammatory cytokines by CD4⁺ T cells. Further study showed that DH didn’t affect the activation, differentiation, and apoptosis of CD4⁺ T cells. Instead, it significantly suppressed the proliferation of conventional CD4⁺ T cells both in vitro and in vivo. Mechanistic study showed that DH-treated CD4⁺ T cells were partially arrested at the G2/M phase of the cell cycle because of the enhanced inhibitory phosphorylation of Cdc2 (Tyr15). In addition, we demonstrated that treatment with DH significantly ameliorated EAU in mice through suppressing the proliferation of autoreactive antigen specific CD4⁺ T cells. Taken together, the current study indicates that DH-mediated suppression of CD4⁺ T cell proliferation may provide a promising therapeutic strategy for treating CD4⁺ T cell–mediated diseases.

COVID-19 TestNorm: A tool to normalize COVID-19 testing names to LOINC codes

Large observational data networks that leverage routine clinical practice data in electronic health records (EHRs) are critical resources for research on coronavirus disease 2019 (COVID-19). Data normalization is a key challenge for the secondary use of EHRs for COVID-19 research across institutions. In this study, we addressed the challenge of automating the normalization of COVID-19 diagnostic tests, which are critical data elements, but for which controlled terminology terms were published after clinical implementation. We developed a simple but effective rule-based tool called COVID-19 TestNorm to automatically normalize local COVID-19 testing names to standard LOINC (Logical Observation Identifiers Names and Codes) codes. COVID-19 TestNorm was developed and evaluated using 568 test names collected from 8 healthcare systems. Our results show that it could achieve an accuracy of 97.4% on an independent test set. COVID-19 TestNorm is available as an open-source package for developers and as an online Web application for end users (https://clamp.uth.edu/covid/loinc.php). We believe that it will be a useful tool to support secondary use of EHRs for research on COVID-19.

User-centered design of a web-based crowdsourcing-integrated semantic text annotation tool for building a mental health knowledge base

BACKGROUND

One in five U.S. adults lives with some kind of mental health condition and 4.6% of all U.S. adults have a serious mental illness. The Internet has become the first place for these people to seek online mental health information for help. However, online mental health information is not well-organized and often of low quality. There have been efforts in building evidence-based mental health knowledgebases curated with information manually extracted from the high-quality scientific literature. Manual extraction is inefficient. Crowdsourcing can potentially be a low-cost mechanism to collect labeled data from non-expert laypeople. However, there is not an existing annotation tool integrated with popular crowdsourcing platforms to perform the information extraction tasks. In our previous work, we prototyped a Semantic Text Annotation Tool (STAT) to address this gap.

OBJECTIVE

We aimed to refine the STAT prototype (1) to improve its usability and (2) to enhance the crowdsourcing workflow efficiency to facilitate the construction of evidence-based mental health knowledgebase, following a user-centered design (UCD) approach.

METHODS

Following UCD principles, we conducted four design iterations to improve the initial STAT prototype. In the first two iterations, usability testing focus groups were conducted internally with 8 participants recruited from a convenient sample, and the usability was evaluated with a modified System Usability Scale (SUS). In the following two iterations, usability testing was conducted externally using the Amazon Mechanical Turk (MTurk) platform. In each iteration, we summarized the usability testing results through thematic analysis, identified usability issues, and conducted a heuristic evaluation to map identified usability issues to Jakob Nielsen's usability heuristics. We collected suggested improvements in the usability testing sessions and enhanced STAT accordingly in the next UCD iteration. After four UCD iterations, we conducted a case study of the system on MTurk using mental health related scientific literature. We compared the performance of crowdsourcing workers with two expert annotators from two aspects: efficiency and quality.

RESULTS

The SUS score increased from 70.3 ± 12.5 to 81.1 ± 9.8 after the two internal UCD iterations as we improved STAT's functionality based on the suggested improvements. We then evaluated STAT externally through MTurk in the following two iterations. The SUS score decreased to 55.7 ± 20.1 in the third iteration, probably because of the complexity of the tasks. After further simplification of STAT and the annotation tasks with an improved annotation guideline, the SUS score increased to 73.8 ± 13.8 in the fourth iteration of UCD. In the evaluation case study, on average, the workers spent 125.5 ± 69.2 s on the onboarding tutorial and the crowdsourcing workers spent significantly less time on the annotation tasks compared to the two experts. In terms of annotation quality, the workers' annotation results achieved average F1-scores ranged from 0.62 to 0.84 for the different sentences.

CONCLUSIONS

We successfully developed a web-based semantic text annotation tool, STAT, to facilitate the curation of semantic web knowledgebases through four UCD iterations. The lessons learned from the UCD process could serve as a guide to further enhance STAT and the development and design of other crowdsourcing-based semantic text annotation tasks. Our study also showed that a well-organized, informative annotation guideline is as important as the annotation tool itself. Further, we learned that a crowdsourcing task should consist of multiple simple microtasks rather than a complicated task.

Optimizing identification of resistant hypertension: Computable phenotype development and validation

PURPOSE

Computable phenotypes are constructed to utilize data within the electronic health record (EHR) to identify patients with specific characteristics; a necessary step for researching a complex disease state. We developed computable phenotypes for resistant hypertension (RHTN) and stable controlled hypertension (HTN) based on the National Patient-Centered Clinical Research Network (PCORnet) common data model (CDM). The computable phenotypes were validated through manual chart review.

METHODS

We adapted and refined existing computable phenotype algorithms for RHTN and stable controlled HTN to the PCORnet CDM in an adult HTN population from the OneFlorida Clinical Research Consortium (2015-2017). Two independent reviewers validated the computable phenotypes through manual chart review of 425 patient records. We assessed precision of our computable phenotypes through positive predictive value (PPV) and test validity through interrater reliability (IRR).

RESULTS

Among the 156 730 HTN patients in our final dataset, the final computable phenotype algorithms identified 24 926 patients with RHTN and 19 100 with stable controlled HTN. The PPV for RHTN in patients randomly selected for validation of the final algorithm was 99.1% (n = 113, CI: 95.2%-99.9%). The PPV for stable controlled HTN in patients randomly selected for validation of the final algorithm was 96.5% (n = 113, CI: 91.2%-99.0%). IRR analysis revealed a raw percent agreement of 91% (152/167) with Cohen's kappa statistic = 0.87.

CONCLUSIONS

We constructed and validated a RHTN computable phenotype algorithm and a stable controlled HTN computable phenotype algorithm. Both algorithms are based on the PCORnet CDM, allowing for future application to epidemiological and drug utilization based research.

Oncologists' Reluctance to Use the Terms Hope and Cure: A Bibliometric Analysis of Articles From Two High-Impact Oncology Journals

The words cure and hope are important terms in oncology, reflecting a balance of aspirations and realism for physicians and patients. Yet, some have suggested that oncologists are reluctant to use these terms. We tested this hypothesis by performing a bibliometric analysis of the frequency of use of these words in JAMA Oncology (JAMA Oncol) and the Journal of Clinical Oncology (JCO). The text of all articles in 3 categories—primary research, editorials, and narrative essays—appearing in JCO from 2000 to 2018 and in JAMA Oncol from 2015 to 2019 was analyzed. These analyses compared, across these categories, the proportion of articles containing the words cure and hope, as well as the proportion of total sentences containing these words. There were statistically significant differences in frequency of the use of the terms cure and hope as a function of the type of article published in the JCO and JAMA Oncol (2-sided P values ranging from .005 to <.001). Results were similar for both journals, with minor exceptions. Both hope and cure were used in a greater number of articles and sentences in the narrative and editorial categories than in primary research. Moreover, hope was used more often in narrative essays than in editorials. The relative reluctance to use these terms in more scientifically oriented original reports, despite concomitant improvements in oncologic outcomes, may reflect a bias worthy of future exploration.

PCSK9 Inhibitor Use in the Real World: Data From the National Patient-Centered Research Network

Background

PCSK9 (proprotein convertase subtilisin/kexin type 9) inhibitors effectively lower LDL (low‐density lipoprotein) cholesterol and have been shown to reduce cardiovascular outcomes in high‐risk patients. We used real‐world electronic health record data to characterize use of PCSK9 inhibitors, in addition to standard therapies, according to cardiovascular risk status.

Methods and Results

Data were obtained from 18 health systems with data marts within the National Patient‐Centered Clinical Research Network (PCORnet) using a common data model. Participating sites identified >17.5 million adults, of whom 3.6 million met study criteria. Patients were categorized into 3 groups: (1) dyslipidemia, (2) untreated LDL ≥130 mg/dL, and (3) coronary artery disease or coronary heart disease. Demographics, comorbidities, estimated 10‐year atherosclerotic cardiovascular disease risk, and lipid‐lowering pharmacotherapies were summarized for each group. Participants’ average age was 62 years, 50% were female, and 11% were black. LDL cholesterol ranged from 85 to 151 mg/dL. Among patients in groups 1 and 3, 54% received standard lipid‐lowering therapies and a PCSK9 inhibitor was prescribed in <1%. PCSK9 inhibitor prescribing was greatest for patients with coronary artery disease or coronary heart disease and, although prescribing increased during the study period, overall PCSK9 inhibitor prescribing was low.

Conclusions

We successfully used electronic health record data from 18 PCORnet data marts to identify >3.6 million patients meeting criteria for 3 patient groups. Approximately half of patients had been prescribed lipid‐lowering medication, but <1% were prescribed PCSK9 inhibitors. PCSK9 inhibitor prescribing increased over time for patients with coronary artery disease or coronary heart disease but not for those with dyslipidemia.

Explainable artificial intelligence models using real-world electronic health record data: a systematic scoping review

OBJECTIVE

To conduct a systematic scoping review of explainable artificial intelligence (XAI) models that use real-world electronic health record data, categorize these techniques according to different biomedical applications, identify gaps of current studies, and suggest future research directions.

MATERIALS AND METHODS

We searched MEDLINE, IEEE Xplore, and the Association for Computing Machinery (ACM) Digital Library to identify relevant papers published between January 1, 2009 and May 1, 2019. We summarized these studies based on the year of publication, prediction tasks, machine learning algorithm, dataset(s) used to build the models, the scope, category, and evaluation of the XAI methods. We further assessed the reproducibility of the studies in terms of the availability of data and code and discussed open issues and challenges.

RESULTS

Forty-two articles were included in this review. We reported the research trend and most-studied diseases. We grouped XAI methods into 5 categories: knowledge distillation and rule extraction (N = 13), intrinsically interpretable models (N = 9), data dimensionality reduction (N = 8), attention mechanism (N = 7), and feature interaction and importance (N = 5).

DISCUSSION

XAI evaluation is an open issue that requires a deeper focus in the case of medical applications. We also discuss the importance of reproducibility of research work in this field, as well as the challenges and opportunities of XAI from 2 medical professionals' point of view.

CONCLUSION

Based on our review, we found that XAI evaluation in medicine has not been adequately and formally practiced. Reproducibility remains a critical concern. Ample opportunities exist to advance XAI research in medicine.

Learning from local to global: An efficient distributed algorithm for modeling time-to-event data

OBJECTIVE

We developed and evaluated a privacy-preserving One-shot Distributed Algorithm to fit a multicenter Cox proportional hazards model (ODAC) without sharing patient-level information across sites.

MATERIALS AND METHODS

Using patient-level data from a single site combined with only aggregated information from other sites, we constructed a surrogate likelihood function, approximating the Cox partial likelihood function obtained using patient-level data from all sites. By maximizing the surrogate likelihood function, each site obtained a local estimate of the model parameter, and the ODAC estimator was constructed as a weighted average of all the local estimates. We evaluated the performance of ODAC with (1) a simulation study and (2) a real-world use case study using 4 datasets from the Observational Health Data Sciences and Informatics network.

RESULTS

On the one hand, our simulation study showed that ODAC provided estimates nearly the same as the estimator obtained by analyzing, in a single dataset, the combined patient-level data from all sites (ie, the pooled estimator). The relative bias was <0.1% across all scenarios. The accuracy of ODAC remained high across different sample sizes and event rates. On the other hand, the meta-analysis estimator, which was obtained by the inverse variance weighted average of the site-specific estimates, had substantial bias when the event rate is <5%, with the relative bias reaching 20% when the event rate is 1%. In the Observational Health Data Sciences and Informatics network application, the ODAC estimates have a relative bias <5% for 15 out of 16 log hazard ratios, whereas the meta-analysis estimates had substantially higher bias than ODAC.

CONCLUSIONS

ODAC is a privacy-preserving and noniterative method for implementing time-to-event analyses across multiple sites. It provides estimates on par with the pooled estimator and substantially outperforms the meta-analysis estimator when the event is uncommon, making it extremely suitable for studying rare events and diseases in a distributed manner.

Roles for intracellular cation transporters in respiratory growth of yeast

Potassium is involved in copper and iron metabolism in eukaryotic Golgi apparatus, but it is not clear yet whether potassium distributions in other vesicles also affect copper and iron metabolism. Here we show that respiratory growth and iron acquisition by the yeast Saccharomyces cerevisiae relies on potassium (K+) compartmentalization to the mitochondria, as well as the vacuole and late endosome via K+/H+ exchangers Mdm38p, Vnx1p and Nhx1p, respectively. The data indicate that NHX1 and VNX1 knock-out cells grow better than wild type cells on non-fermentable YPEG media, while MDM38 knock-out cells display a growth defect on YPEG media. The over expression of the KHA1 gene located on the Golgi apparatus partially compensates for the growth defect of the MDM38 knock-out strain. The results suggest that the vacuole and late endosome are important potassium storage vesicles and Mdm38p affects the mitochondrial function by regulating copper and iron metabolism. Our study reveals potassium compartmentalization to the subcellular vesicles is relevant for respiratory growth by improving copper utilization and promoting iron absorption.

Geographic variation in palliative care delivery among patients diagnosed with metastatic lung cancer in the USA: Medicare population-based study

PURPOSE

The USA has observed a significant increase in the use of palliative care for patients diagnosed with advanced cancer. However, it is unknown how geographic variation affects patients' use of palliative care services. We examined temporal and demographic trends in receipt of and timing of palliative care by state and region.

METHODS

A retrospective cohort study of the Surveillance, Epidemiology, and End Results (SEER)-Medicare database. Study sample included community-dwelling patients aged ≥ 65 years with metastatic lung cancer who were diagnosed between 2001 and 2015. Cochran-Armitage trend test was used to evaluate temporal trends in receipt of and timing of palliative care by states and census region.

RESULTS

The proportion of metastatic lung cancer patients who received palliative care ranged from 16.4% in Washington and 16.3% in Connecticut to 6.4% in Louisiana. From 2001 to 2015, use of palliative care increased from 3.2 to 29.8% in the West region, from 3.3 to 31.9% in the Northeast region, from 3.8 to 36.2% in the Midwest region, and from 0.9 to 23.3% in the South region (all P < 0.001). The median time from the date of cancer diagnosis to the date of first palliative care visit varied geographically, from 44 days in Utah to 66 days in California. Hospital-based palliative care was most common in these states.

CONCLUSION

The substantial geographic variation in the use of palliative care suggesting a need for additional research on geographic disparities in palliative care and strategies that might improve state-level palliative care delivery.

Differential proteomic analysis of fetal and geriatric lumbar nucleus pulposus: immunoinflammation and age-related intervertebral disc degeneration

BACKGROUND

Intervertebral disc degeneration (IVDD) is a major cause of low back pain. Although the mechanism of degeneration remains unclear, aging has been recognized as a key risk factor for IVDD. Most studies seeking to identify IVDD-associated molecular alterations in the context of human age-related IVDD have focused only on a limited number of proteins. Differential proteomic analysis is an ideal method for comprehensively screening altered protein profiles and identifying the potential pathways related to pathological processes such as disc degeneration.

METHODS

In this study, tandem mass tag (TMT) labeling was combined with liquid chromatography-tandem mass spectrometry (LC-MS/MS) for differential proteomic analysis of human fetal and geriatric lumbar disc nucleus pulposus (NP) tissue. Parallel reaction monitoring (PRM) and Western blotting (WB) techniques were used to identify target proteins. Bioinformatic analyses, including Gene Ontology (GO) annotation, domain annotation, pathway annotation, subcellular localization and functional enrichment analyses, were used to interpret the potential significance of the protein alterations in the mechanism of IVDD. Student's t-tests and two-tailed Fisher's exact tests were used for statistical analysis.

RESULTS

Six hundred forty five proteins were significantly upregulated and 748 proteins were downregulated in the geriatric group compared with the fetal group. Twelve proteins were verified to have significant differences in abundance between geriatric and fetal NP tissue; most of these have not been previously identified as being associated with human IVDD. The potential significance of the differentially expressed proteins in age-related IVDD was analyzed from multiple perspectives, especially with regard to the association of the immunoinflammatory response with IVDD.

CONCLUSIONS

Differential proteomic analysis was used as a comprehensive strategy for elucidating the protein alterations associated with age-related IVDD. The findings of this study will aid in the screening of new biomarkers and molecular targets for the diagnosis and therapy of IVDD. The results may also significantly enhance our understanding of the pathophysiological process and mechanism of age-related IVDD.

The preemptive analgesia of pre-electroacupuncture in rats with formalin-induced acute inflammatory pain

Background

Electroacupuncture has been elicited to effectively alleviate the pain sensation. Preemptive analgesic effect of pre-electroacupuncture has also been suggested in recent studies, while the underlying analgesic mechanism of pre-electroacupuncture requires further investigation. This study aimed to explore the preemptive analgesia of pre-electroacupuncture in formalin-induced acute inflammatory pain model.

Methods

Forty rats were randomly divided into control, model, pre-electroacupuncture, and post-electroacupuncture group. Inflammatory pain model was induced via injecting 50 µl 5% formalin into the plantar surface of right hind paw, while the equal volume of saline injection in the control group. Rats in the pre-electroacupuncture group were treated with electroacupuncture at ipsilateral Zusanli (ST36) and Weizhong (BL40) acupoints (2 Hz, 1 mA) for 30 min before formalin injection, while received the same electroacupuncture treatment immediately after formalin injection in the post-electroacupuncture group. Flinching number and licking time were recorded during 60 min after formalin injection. Immunofluorescence and Western blot were used to detect the expression of ionized calcium binding adapter molecule 1 (Iba1) and c-fos in spinal cord. Moreover, enzyme-linked immunosorbent assay was applied to measure the secretion of IL-6, IFN-γ, IL-4, substance P, and calcitonin gene-related peptide in spinal cord.

Results

Paw flinching and licking were obviously induced by formalin injection. Iba1, c-fos, proinflammatory cytokines (IL-6 and IFN-γ), and pain neurotransmitters (substance P and calcitonin gene-related peptide) were dramatically increased in the L4-5 spinal cord after formalin injection, while anti-inflammatory cytokine IL-4 was decreased. Pre-electroacupuncture and post-electroacupuncture administration significantly attenuated formalin-induced nociceptive effects, spinal microglia and neurons activation, proinflammatory cytokines and pain neurotransmitters upregulation, and upregulated the anti-inflammatory cytokine. Furthermore, these effects of pre-electroacupuncture were more significant than that of post-electroacupuncture.

Conclusions

This study illustrates the potential therapeutic effect of pre-electroacupuncture against acute inflammatory pain and reveals the mechanism underlying pre-electroacupuncture mediated analgesia, thus providing a novel preemptive analgesic treatment.

Abstract C002: Racial and geographic variation in knowledge of palliative care among American adults

Introduction Palliative care provides clinical and economic benefits for patients diagnosed with life limiting illness and their family caregivers. The extent to which variation in knowledge of palliative care exits in racial groups and geographic regions within the United States is not known. The aim of the study was to present the up-to-date data on the knowledge penetration of palliative care by racial and geographic regions. Methodology We assessed variations in knowledge of palliative care using the 2018 National Cancer Institute’s Health Information National Trends Survey. We used the Pearson chi-square test and multivariable logistic regression models to assess the association of race and having knowledge of palliative care for each census geographic region. The state-level prevalence of no knowledge of palliative care were plotted in a map. Results The study population included 3194 respondents (weighted sample size: 229,591,005; median age: 58). About 15 % of the study population was Hispanic, 10% non-Hispanic-Black, and 61% non-Hispanic White. About 84% Hispanic respondents, 75% non-Hispanic Blacks and 65% non-Hispanic Whites had no knowledge of palliative care (P &lt;0.001). For Hispanic, the prevalence of no knowledge of palliative care ranged from 48% in East South Central region to 96% in East North Central and West North Central region. For non-Hispanic Blacks, the prevalence of no knowledge of palliative care ranged from 32% in New England region to 97% in West North Central region. For non-Hispanic Whites, the prevalence of no knowledge of palliative care ranged from 44% in New England region to 78% in Mountain region. Both racial group and census geographic regions were statistically significant variables in the multivariable model predicting no knowledge of palliative care. Conclusions In the United States, substantial geographic variations in the knowledge of palliative care exist. The prevalence of responders who had no knowledge of palliative care were greater in Hispanic and non-Hispanic Black than non-Hispanic White groups. This finding represents an opportunity for targeted future education to increase the knowledge gap overall and in patients of non-White decent.

Citation Format: Kayanna Jacobs, Young Rock Hong, Jiang Bian, Sheri Kittelson, Diana J. Wilkie, Jinhai Huo. Racial and geographic variation in knowledge of palliative care among American adults [abstract]. In: Proceedings of the Twelfth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2019 Sep 20-23; San Francisco, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(6 Suppl_2):Abstract nr C002.

Mining Twitter to Assess the Determinants of Health Behavior towards Palliative Care in the United States

Palliative care is a specialized service with proven efficacy in improving patients' quality-of-life. Nevertheless, lack of awareness and misunderstanding limits its adoption. Research is urgently needed to understand the determinants (e.g., knowledge) related to its adoption. Traditionally, these determinants are measured with questionnaires. In this study, we explored Twitter to reveal these determinants guided by the Integrated Behavioral Model. A secondary goal is to assess the feasibility of extracting user demographics from Twitter data-a significant shortcoming in existing studies that limits our ability to explore more fine-grained research questions (e.g., gender difference). Thus, we collected, preprocessed, and geocoded palliative care-related tweets from 2013 to 2019 and then built classifiers to: 1) categorize tweets into promotional vs. consumer discussions, and 2) extract user gender. Using topic modeling, we explored whether the topics learned from tweets are comparable to responses of palliative care-related questions in the Health Information National Trends Survey.

Significant Day-time Ionospheric Perturbation by Thunderstorms along the West African and Congo Sector of Equatorial Region

The equatorial Congo has been recognized as the most active lightning chimney region in the Globe. Although the perturbation of tropospheric thunderstorms on the lower ionosphere has been noticed in the middle latitudes through their transient lightning electric fields or convective gravity waves, the effects on equatorial ionosphere and the horizontal extent of this perturbation remains a mystery because of the difficulties in extracting the effects due to the sporadic nature of the equatorial ionosphere. Here we present observational results showing solid evidence of deviations in ionospheric total electron content (TEC) and its direction of propagation associated with thunderstorms using the method of polynomial filtering, by utilizing the TEC measured from equatorial Global Positioning System (GPS) Receiver stations along the West African region-Congo Basin. The TEC deviations due to the thunderstorms were found to be mostly propagated in a specific direction from the point of the event, with the highest absolute peak TEC at ~±1.5 TECUs. The internal dynamics of the equatorial ionosphere have been found to be suppressed by large thunderstorm effects during the daytime, with negligible impact at night.

[Dihydrotanshinone I (DHTS1) attenuates cuprizone-induced demyelination via regulating microglia polarization]

Objective To evaluate whether dihydrotanshinone I (DHTS1) attenuates cuprizone-induced demyelination. Methods DHTS1 was dissolved in 5 g/L sodium carboxymethyl cellulose (CMC-Na). The cuprizone model was induced via feeding with the diet containing 2 g/L cuprizone. We administrated DHTS1 to the cuprizone-exposed mice. The mice were randomly divided into CMC-Na normal group, CMC-Na combined with cuprizone group and DHTS1 combined with cuprizone group. Myelin degeneration was checked by Luxol fast blue (LFB) staining and the immunohistochemical staining of myelin basic protein (MBP) and myelin proteolipid (PLP). Cell apoptosis was measured by TUNEL. Microglia polarization was evaluated by Iba-1, CD86 and CD163 immunohistochemical staining in vivo. The SIM-A9 cells cultured were divided into CMC-Na group, DHTS1 group, CMC-Na combined with LPS group and DHTS1 combined with LPS group. The expression of CD16/32, tumor necrosis factor-α (TNF-α), inducible nitric oxide synthase (iNOS) was analyzed by flow cytometry in vitro. Results Compared with CMC-Na combined with cuprizone group, DHTS1 treatment significantly attenuated myelin loss and cell apoptosis, reduced the area of Iba-1⁺ amoebic microglia and the number of CD86⁺ cells, while increased the number of CD163⁺ cells in the corpus callosum area of the brain. In addition, compared with CMC-Na combined with LPS group, DHTS1 obviously decreased the percentages of CD16/32⁺, iNOS⁺, TNF-α⁺ microglia. Conclusion DHTS1 can suppress cuprizone-induced demyelination and cell apoptosis through regulating the microglia polarization and mitigating inflammatory reaction in the central nerve system.