Polygenic risk of Alzheimer's disease in the Faroe Islands

INTRODUCTION

The Faroe Islands are a geographically isolated population in the North Atlantic with a similar prevalence Alzheimer's disease (AD) and all cause dementia as other European nations. However, the genetic risk underlying Alzheimer's disease and other dementia susceptibility has yet to be elucidated.

METHODS

Forty-nine single nucleotide polymorphisms (SNPs) were genotyped in 174 patients with AD and other dementias and 159 healthy controls. Single variant and polygenic risk score (PRS) associations, with/without APOE variability, were assessed by logistic regression. Performance was examined using receiver operating characteristics 'area under the curve' analysis (ROC AUC).

RESULTS

APOE rs429358 was associated with AD in the Faroese cohort after correction for multiple testing (OR=6.32, CI[3.98-10.05], p=6.31e^-15 ), with suggestive evidence for three other variants: NECTIN2 rs41289512 (OR 2.05, CI[1.20-3.51], p=0.01), HLA-DRB1 rs6931277 (OR 0.67, CI[0.48-0.94], p=0.02), and APOE rs7412 [ε2] (OR 0.28, CI[0.11-0.73], p=0.01). PRS were associated with AD with or without the inclusion of APOE (PRS^+APOE OR=4.5. CI[2.90-5.85, p=4.56e-¹⁵ and PRS^-APOE OR=1.53, CI[1.21-1.98], p=6.82e^-4 ). AD ROC AUC analyses demonstrated a PRS^+APOE AUC=80.3% and PRS^-APOE AUC=63.4%. However, PRS^+APOE was also significantly associated with all cause dementia (OR=3.39, CI[2.51-4.71], p= 2.50e^-14 ) with an AUC=76.9%, i.e. all cause dementia did show similar results albeit less significant.

DISCUSSION

In the Faroe Islands, SNP analyses highlighted APOE and immunogenomic variability in AD and dementia risk. PRS^+APOE , based on 25 SNPs/loci, had excellent sensitivity and specificity for Alzheimer's disease with AUC of 80.3%. High PRS were also associated with an earlier onset of late-onset AD.

2-Methoxy-5((3,4,5-trimethosyphenyl) seleninyl) phenol causes G2/M cell cycle arrest and apoptosis in NSCLC cells through mitochondrial apoptotic pathway and MDM2 inhibition

Nonsmall cell lung cancer (NSCLC) is one of the most common malignancies and needs novel and effective chemotherapy. In this study, our purpose is to explore the anticancer effects of 2-methoxy-5((3,4,5-trimethosyphenyl) seleninyl) phenol (SQ) on human NSCLC (A549 and H460) cells. We found that SQ suppressed the proliferation of NSCLC cells in time- and dose-dependent manners, and blocked the cells at G2/M phase, which was relevant to microtubule depolymerization. Additionally, SQ induced A549 and H460 cell apoptosis by activating the mitochondrial apoptotic pathway. Further, we demonstrated that SQ enhanced the generation of reactive oxygen species (ROS), and pretreatment with N-acetyl- L-cysteine (NAC) attenuated SQ-induced cell apoptosis. Meanwhile, SQ mediated-ROS generation caused DNA damage in A549 and H460 cells. Our data also revealed that SQ-induced apoptosis was correlated with the inhibition of mouse double minute 2 (MDM2) in A549 and H460 cells. In summary, our research indicates that the novel compound SQ has great potential for therapeutic treatment of NSCLC in future.

DLMM as a lossless one-shot algorithm for collaborative multi-site distributed linear mixed models

Linear mixed models are commonly used in healthcare-based association analyses for analyzing multi-site data with heterogeneous site-specific random effects. Due to regulations for protecting patients’ privacy, sensitive individual patient data (IPD) typically cannot be shared across sites. We propose an algorithm for fitting distributed linear mixed models (DLMMs) without sharing IPD across sites. This algorithm achieves results identical to those achieved using pooled IPD from multiple sites (i.e., the same effect size and standard error estimates), hence demonstrating the lossless property. The algorithm requires each site to contribute minimal aggregated data in only one round of communication. We demonstrate the lossless property of the proposed DLMM algorithm by investigating the associations between demographic and clinical characteristics and length of hospital stay in COVID-19 patients using administrative claims from the UnitedHealth Group Clinical Discovery Database. We extend this association study by incorporating 120,609 COVID-19 patients from 11 collaborative data sources worldwide.

A lossless, one-shot and privacy-preserving distributed algorithm was revealed for fitting linear mixed models on multi-site data. The algorithm was applied to a study of 120,609 COVID-19 patients using only minimal aggregated data from each of 14 sites.

Unraveling COVID-19: A Large-Scale Characterization of 4.5 Million COVID-19 Cases Using CHARYBDIS

Purpose

Routinely collected real world data (RWD) have great utility in aiding the novel coronavirus disease (COVID-19) pandemic response. Here we present the international Observational Health Data Sciences and Informatics (OHDSI) Characterizing Health Associated Risks and Your Baseline Disease In SARS-COV-2 (CHARYBDIS) framework for standardisation and analysis of COVID-19 RWD.

Patients and Methods

We conducted a descriptive retrospective database 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 11th June 2020 and are iteratively updated via GitHub. We identified three non-mutually exclusive cohorts of 4,537,153 individuals with a clinical COVID-19 diagnosis or positive test, 886,193 hospitalized with COVID-19, and 113,627 hospitalized with COVID-19 requiring intensive services.

Results

We aggregated over 22,000 unique characteristics describing patients with COVID-19. All comorbidities, symptoms, medications, and outcomes are described by cohort in aggregate counts and are readily available online. Globally, we observed similarities in the USA and Europe: more women diagnosed than men but more men hospitalized than women, most diagnosed cases between 25 and 60 years of age versus most hospitalized cases between 60 and 80 years of age. South Korea differed with more women than men hospitalized. Common comorbidities included type 2 diabetes, hypertension, chronic kidney disease and heart disease. Common presenting symptoms were dyspnea, cough and fever. Symptom data availability was more common in hospitalized cohorts than diagnosed.

Conclusion

We constructed a global, multi-centre view to describe trends in COVID-19 progression, management and evolution over time. By characterising baseline variability in patients and geography, our work provides critical context that may otherwise be misconstrued as data quality issues. This is important as we perform studies on adverse events of special interest in COVID-19 vaccine surveillance.

Identifying Patients at Risk of Acute Kidney Injury Among Medicare Beneficiaries With Type 2 Diabetes Initiating SGLT2 Inhibitors: A Machine Learning Approach

Introduction: To predict acute kidney injury (AKI) risk in patients with type 2 diabetes (T2D) prescribed sodium-glucose cotransporter two inhibitors (SGLT2i).

Methods: Using a 5% random sample of Medicare claims data, we identified 17,694 patients who filled ≥1 prescriptions for canagliflozin, dapagliflozin and empagliflozin in 2013–2016. The cohort was split randomly and equally into training and testing sets. We measured 65 predictor candidates using claims data from the year prior to SGLT2i initiation. We then applied three machine learning models, including random forests (RF), elastic net and least absolute shrinkage and selection operator (LASSO) for risk prediction.

Results: The incidence rate of AKI was 1.1% over a median 1.5 year follow up. Among three machine learning methods, RF produced the best prediction (C-statistic = 0.72), followed by LASSO and elastic net (both C-statistics = 0.69). Among individuals classified in the top 10% of the RF risk score (i.e., high risk group), the actual incidence rate of AKI was as high as 3.7%. In the logistic regression model including 14 important risk factors selected by LASSO, use of loop diuretics [adjusted odds ratio (95% confidence interval): 3.72 (2.44–5.76)] had the strongest association with AKI incidence.

Disscusion: Our machine learning model efficiently identified patients at risk of AKI among Medicare beneficiaries with T2D undergoing SGLT2i treatment.

Procedural complications associated with invasive diagnostic procedures after lung cancer screening with low-dose computed tomography

INTRODUCTION

Although the National Lung Screening Trial (NLST) has proven low-dose computed tomography (LDCT) is effective for lung cancer screening, little is known about complication rates from invasive diagnostic procedures (IDPs) after LDCT in real-world settings. In this study, we used the real-world data from a large clinical research network to estimate the complication rates associated with IDPs after LDCT.

METHODS

Using 2014-2021 electronic health records and claims data from the OneFlorida clinical research network, we identified case individuals who underwent an IDP (i.e., cytology or needle biopsy, bronchoscopy, thoracic surgery, and other surgery) within 12 months of their first LDCT. We matched each case with one control individual who underwent an LDCT but without any IDPs. We calculated 3-month incremental complication rates as the difference in the complication rate between the case and control groups by IDP and complication severity.

RESULTS

Among 7,041 individuals who underwent an LDCT, 301 (4.3%) subsequently had an IDP within 12 months following the LDCT. The overall 3-month incremental complication rate was 16.6% (95% confidence interval [CI]: 9.9% - 23.1%), higher than that reported in the NLST (9.4%). The overall incremental complication rate was 5.6% (95% CI: 1.9% - 9.6%) for major, 8.6% (95% CI: 3.1% - 14.1%) for intermediate, and 13.2% (95% CI: 8.1% - 18.5%) for minor complications.

CONCLUSIONS

It is important to ensure adherence to clinical guidelines for nodule management and downstream work-up to minimize potential harms from screening.

Data and Model Biases in Social Media Analyses: A Case Study of COVID-19 Tweets

During the coronavirus disease pandemic (COVID-19), social media platforms such as Twitter have become a venue for individuals, health professionals, and government agencies to share COVID-19 information. Twitter has been a popular source of data for researchers, especially for public health studies. However, the use of Twitter data for research also has drawbacks and barriers. Biases appear everywhere from data collection methods to modeling approaches, and those biases have not been systematically assessed. In this study, we examined six different data collection methods and three different machine learning (ML) models-commonly used in social media analysis-to assess data collection bias and measure ML models' sensitivity to data collection bias. We showed that (1) publicly available Twitter data collection endpoints with appropriate strategies can collect data that is reasonably representative of the Twitter universe; and (2) careful examinations of ML models' sensitivity to data collection bias are critical.

Validation of Real-World Data-based Endpoint Measures of Cancer Treatment Outcomes

Recently, there has been a growing interest in using real-world data (RWD) to generate real-world evidence that complements clinical trials. To quantify treatment effects, it is important to develop meaningful RWD-based endpoints. In cancer trials, two real-world endpoints are of particular interest: real-world overall survival (rwOS) and real-world time to next treatment (rwTTNT). In this work, we identified ways to calculate these real-world endpoints with structured electronic health record (EHR) data and validate these endpoints against the gold-standard measurements of these endpoints derived from linked EHR and tumor registry (TR) data. In addition, we examined and reported data quality issues, especially inconsistencies between the EHR and TR data. Using a survival model, we show that the presence of next treatment was not significantly associated with rwOS, but patients who had longer rwTTNT had longer rwOS, validating the use of rwTTNT as a real-world surrogate marker for measuring cancer endpoints.

Environmental effects on acute exacerbations of respiratory diseases: A real-world big data study

BACKGROUND

The effects of weather periods, race/ethnicity, and sex on environmental triggers for respiratory exacerbations are not well understood. This study linked the OneFlorida network (~15 million patients) with an external exposome database to analyze environmental triggers for asthma, bronchitis, and COPD exacerbations while accounting for seasonality, sex, and race/ethnicity.

METHODS

This is a case-crossover study of OneFlorida database from 2012 to 2017 examining associations of asthma, bronchitis, and COPD exacerbations with exposures to heat index, PM 2.5 and O 3. We spatiotemporally linked exposures using patients' residential addresses to generate average exposures during hazard and control periods, with each case serving as its own control. We considered age, sex, race/ethnicity, and neighborhood deprivation index as potential effect modifiers in conditional logistic regression models.

RESULTS

A total of 1,148,506 exacerbations among 533,446 patients were included. Across all three conditions, hotter heat indices conferred increasing exacerbation odds, except during November to March, where the opposite was seen. There were significant differences when stratified by race/ethnicity (e.g., for asthma in April, May, and October, heat index quartile 4, odds were 1.49 (95% confidence interval (CI) 1.42-1.57) for Non-Hispanic Blacks and 2.04 (95% CI 1.92-2.17) for Hispanics compared to 1.27 (95% CI 1.19-1.36) for Non-Hispanic Whites). Pediatric patients' odds of asthma and bronchitis exacerbations were significantly lower than adults in certain circumstances (e.g., for asthma during June - September, pediatric odds 0.71 (95% CI 0.68-0.74) and adult odds 0.82 (95% CI 0.79-0.85) for the highest quartile of PM 2.5).

CONCLUSION

This study of acute exacerbations of asthma, bronchitis, and COPD found exacerbation risk after exposure to heat index, PM 2.5 and O 3 varies by weather period, age, and race/ethnicity. Future work can build upon these results to alert vulnerable populations to exacerbation triggers.

Novel microtubule inhibitor SQ overcomes multidrug resistance in MCF-7/ADR cells by inhibiting BCRP function and mediating apoptosis

The occurrence of multidrug resistance (MDR) is one of the impediments in the clinical treatment of breast cancer, and MDR breast cancer has abnormally high breast cancer resistance protein (BCRP/ABCG2) expression. However, there are currently no clinical drugs that inhibit this target. Our previous study found that 2-Methoxy-5((3,4,5-trimethosyphenyl)seleninyl) phenol (SQ0814061/SQ), a small molecule drug with low toxicity to normal tissues, could target microtubules, inhibit the proliferation of breast cancer, and reduce its migration and invasion abilities. However, the effect and the underlying mechanism of SQ on MDR breast cancers are still unknown. Therefore, in this study, we investigated the effect of SQ on adriamycin-resistant MCF-7 (MCF-7/ADR) cells and explored the underlying mechanism. The MTT assay showed that SQ had potent cytotoxicity to MCF-7/ADR cells. In particular, the results of western blot and flow cytometry proved that SQ could effectively inhibit the expression of BCRP in MCF-7/ADR cells to decrease its drug delivery activity. In addition, SQ could block the cell cycle at G2/M phase in parental and MCF-7/ADR cells, thereby mediating cell apoptosis, which was related with the inhibition of PI3K-Akt-MDM2 pathway. Taken together, our findings indicate that SQ overcomes multidrug resistance in MCF-7/ADR cells by inhibiting BCRP function and mediating apoptosis through PI3K-Akt-MDM2 pathway inhibition.

Mimicking atmospheric photochemical modelling with a deep neural network

Fast and accurate prediction of ambient ozone (O₃) formed from atmospheric photochemical processes is crucial for designing effective O₃ pollution control strategies in the context of climate change. The chemical transport model (CTM) is the fundamental tool for O₃ prediction and policy design, however, existing CTM-based approaches are computationally expensive, and resource burdens limit their usage and effectiveness in air quality management. Here we proposed a novel method (noted as DeepCTM) that using deep learning to mimic CTM simulations to improve the computational efficiency of photochemical modeling. The well-trained DeepCTM successfully reproduces CTM-simulated O₃ concentration using input features of precursor emissions, meteorological factors, and initial conditions. The advantage of the DeepCTM is its high efficiency in identifying the dominant contributors to O₃ formation and quantifying the O₃ response to variations in emissions and meteorology. The emission-meteorology-concentration linkages implied by the DeepCTM are consistent with known mechanisms of atmospheric chemistry, indicating that the DeepCTM is also scientifically reasonable. The DeepCTM application in China suggests that O₃ concentrations are strongly influenced by the initialized O₃ concentration, as well as emission and meteorological factors during daytime when O₃ is formed photochemically. The variation of meteorological factors such as short-wave radiation can also significantly modulate the O₃ chemistry. The DeepCTM developed in this study exhibits great potential for efficiently representing the complex atmospheric system and can provide policymakers with urgently needed information for designing effective control strategies to mitigate O₃ pollution.

Assessment of structured data elements for social risk factors

OBJECTIVES

Computable social risk factor phenotypes derived from routinely collected structured electronic health record (EHR) or health information exchange (HIE) data may represent a feasible and robust approach to measuring social factors. This study convened an expert panel to identify and assess the quality of individual EHR and HIE structured data elements that could be used as components in future computable social risk factor phenotypes.

STUDY DESIGN

Technical expert panel.

METHODS

A 2-round Delphi technique included 17 experts with an in-depth knowledge of available EHR and/or HIE data. The first-round identification sessions followed a nominal group approach to generate candidate data elements that may relate to socioeconomics, cultural context, social relationships, and community context. In the second-round survey, panelists rated each data element according to overall data quality and likelihood of systematic differences in quality across populations (ie, bias).

RESULTS

Panelists identified a total of 89 structured data elements. About half of the data elements (n = 45) were related to socioeconomic characteristics. The panelists identified a diverse set of data elements. Elements used in reimbursement-related processes were generally rated as higher quality. Panelists noted that several data elements may be subject to implicit bias or reflect biased systems of care, which may limit their utility in measuring social factors.

CONCLUSIONS

Routinely collected structured data within EHR and HIE systems may reflect patient social risk factors. Identifying and assessing available data elements serves as a foundational step toward developing future computable social factor phenotypes.

Effects of brief exposure to misinformation about e-cigarette harms on Twitter on knowledge and perceptions of e-cigarettes

Background

This study examined whether exposure to misinformation found on Twitter about e-cigarette harms leads to inaccurate knowledge and misperceptions of harms of e-cigarette use among cigarette smokers.

Methods

We conducted an online randomized controlled experiment in November 2019 among an online sample of 2400 adult US and UK cigarette smokers who did not currently use e-cigarettes. Participants viewed four tweets in one of four conditions: 1) e-cigarettes are as or more harmful than smoking, 2) e-cigarettes are completely harmless, 3) e-cigarette harms are uncertain and 4) control (physical activity). Outcomes were knowledge about e-cigarettes and harm perceptions of e-cigarette use for five diseases. We conducted multiple logistic and linear regressions to analyze the effect of experimental conditions on outcomes, controlling for baseline knowledge and perceived harms.

Findings

Participants in the ‘as or more harmful’ condition (vs. control group) had higher odds of accurate knowledge about e-cigarettes containing toxic chemicals ( p < 0.001), not containing only water vapor (p < 0.001) and containing formaldehyde ( p < 0.001). However, these participants had lower odds of accurate knowledge that e-cigarettes did not contain tar ( p < 0.001) and contained fewer toxins than cigarettes ( p < 0.001). Exposure to ‘as or more harmful’ tweets also increased harm perceptions for five diseases (all p < 0.001), with the greatest effect observed for lung cancer (β = 0.313, p < 0.001). This effect was greater among UK participants for all diseases.

Interpretation

Brief exposure to misinformation on Twitter reduced accurate knowledge of the presence of tar and the level of toxins compared with smoking and increased harm perceptions of e-cigarette use.

Temporal Subtyping of Alzheimer's Disease Using Medical Conditions Preceding Alzheimer's Disease Onset in Electronic Health Records

Subtyping of Alzheimer's disease (AD) can facilitate diagnosis, treatment, prognosis and disease management. It can also support the testing of new prevention and treatment strategies through clinical trials. In this study, we employed spectral clustering to cluster 29,922 AD patients in the OneFlorida Data Trust using their longitudinal EHR data of diagnosis and conditions into four subtypes. These subtypes exhibit different patterns of progression of other conditions prior to the first AD diagnosis. In addition, according to the results of various statistical tests, these subtypes are also significantly different with respect to demographics, mortality, and prescription medications after the AD diagnosis. This study could potentially facilitate early detection and personalized treatment of AD as well as data-driven generalizability assessment of clinical trials for AD.

Moving from predicting hospital deaths by antibiotic-resistant bloodstream bacteremia toward actionable risk reduction using machine learning on electronic health records

Drug-resistant bacterial infections are a global health concern with high mortality and limited treatment options. Several clinical risk-severity scores are available, e.g. qPitt, but their predictive performance is moderate. Here, we leveraged machine learning and electronic health records (EHRs) to improve prediction of mortality due to bloodstream infection with Klebsiella pneumoniae. We tested the qPitt score and new EHR variables (either expert-chosen or the full set of diagnostic codes), fitting LASSO, boosted logistic regression (BLR), support vector machines, decision trees, and random forests. The qPitt score showed moderate discriminative ability (AUROC=0.63), whilst machine learning models significantly improved its performance (best AUROC by BLR 0.80 for expert-chosen and 0.88 for full code set). Similar results were obtained in critically ill patients, and when excluding potential non-causal variables to evaluate an actionable model. In conclusion, current risk scores for bacteremia mortality can be improved and, with opportune causal modelling, considered for deployment in clinical decision-making.

Impacts of Eligibility Criteria on Trial Participants' Age in Alzheimer's Disease Clinical Trials

Overly restricted and poorly designed eligibility criteria reduce the generalizability of the results from clinical trials. We conducted a study to identify and quantify the impacts of study traits extracted from eligibility criteria on the age of study populations in Alzheimer's Disease (AD) clinical trials. Using machine learning methods and SHapley Additive exPlanation (SHAP) values, we identified 30 and 34 study traits that excluded older patients from AD trials in our 2 generated target populations respectively. We also found that study traits had different magnitudes of impacts on the age distributions of the generated study populations across racial-ethnic groups. To our best knowledge, this was the first study that quantified the impact of eligibility criteria on the age of AD trial participants. Our research is a first step in addressing the overly restrictive eligibility criteria in AD clinical trials.

Smokers' Likelihood to Engage With Information and Misinformation on Twitter About the Relative Harms of e-Cigarette Use: Results From a Randomized Controlled Trial

Background

Information and misinformation on the internet about e-cigarette harms may increase smokers’ misperceptions of e-cigarettes. There is limited research on smokers’ engagement with information and misinformation about e-cigarettes on social media.

Objective

This study assessed smokers’ likelihood to engage with—defined as replying, retweeting, liking, and sharing—tweets that contain information and misinformation and uncertainty about the harms of e-cigarettes.

Methods

We conducted a web-based randomized controlled trial among 2400 UK and US adult smokers who did not vape in the past 30 days. Participants were randomly assigned to view four tweets in one of four conditions: (1) e-cigarettes are as harmful or more harmful than smoking, (2) e-cigarettes are completely harmless, (3) uncertainty about e-cigarette harms, or (4) control (physical activity). The outcome measure was participants’ likelihood of engaging with tweets, which comprised the sum of whether they would reply, retweet, like, and share each tweet. We fitted Poisson regression models to predict the likelihood of engagement with tweets among 974 Twitter users and 1287 non-Twitter social media users, adjusting for covariates and stratified by UK and US participants.

Results

Among Twitter users, participants were more likely to engage with tweets in condition 1 (e-cigarettes are as harmful or more harmful than smoking) than in condition 2 (e-cigarettes are completely harmless). Among other social media users, participants were more likely to likely to engage with tweets in condition 1 than in conditions 2 and 3 (e-cigarettes are completely harmless and uncertainty about e-cigarette harms).

Conclusions

Tweets stating information and misinformation that e-cigarettes were as harmful or more harmful than smoking regular cigarettes may receive higher engagement than tweets indicating e-cigarettes were completely harmless.

Trial Registration

International Standard Randomized Controlled Trial Number (ISRCTN) 16082420; https://doi.org/10.1186/ISRCTN16082420

Sequencing of Argonaute-bound microRNA/mRNA hybrids reveals regulation of the unfolded protein response by microRNA-320a

MicroRNAs (miRNA) are short non-coding RNAs widely implicated in gene regulation. Most metazoan miRNAs utilize the RNase III enzymes Drosha and Dicer for biogenesis. One notable exception is the RNA polymerase II transcription start sites (TSS) miRNAs whose biogenesis does not require Drosha. The functional importance of the TSS-miRNA biogenesis is uncertain. To better understand the function of TSS-miRNAs, we applied a modified Crosslinking, Ligation, and Sequencing of Hybrids on Argonaute (AGO-qCLASH) to identify the targets for TSS-miRNAs in HCT116 colorectal cancer cells with or without DROSHA knockout. We observed that miR-320a hybrids dominate in TSS-miRNA hybrids identified by AGO-qCLASH. Targets for miR-320a are enriched for the eIF2 signaling pathway, a downstream component of the unfolded protein response. Consistently, in miR-320a mimic- and antagomir- transfected cells, differentially expressed gene products are associated with eIF2 signaling. Within the AGO-qCLASH data, we identified the endoplasmic reticulum (ER) chaperone calnexin as a direct miR-320a down-regulated target, thus connecting miR-320a to the unfolded protein response. During ER stress, but not amino acid deprivation, miR-320a up-regulates ATF4, a critical transcription factor for resolving ER stress. In summary, our study investigates the targetome of the TSS-miRNAs in colorectal cancer cells and establishes miR-320a as a regulator of unfolded protein response.

Discovery of styrylaniline derivatives as novel alpha-synuclein aggregates ligands

Parkinson's disease (PD) is the second most common neurodegenerative disorder. Early diagnosis is the key to treatment but is still a great challenge in the clinic now. The discovery of alpha-synuclein (α-syn) aggregates ligands has become an attractive strategy to meet the early diagnosis of PD. Herein, we designed and synthesized a series of styrylaniline derivatives as novel α-syn aggregates ligands. Several compounds displayed good potency to α-syn aggregates with K_d values less than 0.1 μM. The docking study revealed that the hydrogen bonds and cation-pi interaction between ligands and α-syn aggregates would be crucial for the activity. The representative compound 7-16 not only detected α-syn aggregates in both SH-SY5Y cells and brain tissues prepared from two kinds of α-syn preformed-fibrils-injected mice models but also showed good blood-brain barrier penetration characteristics in vivo with a brain/plasma ratio over 1.0, which demonstrates its potential as a lead compound for further development of in vivo imaging agents.

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

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.

Extracting social determinants of health from electronic health records using natural language processing: a systematic review

OBJECTIVE

Social determinants of health (SDoH) are nonclinical dispositions that impact patient health risks and clinical outcomes. Leveraging SDoH in clinical decision-making can potentially improve diagnosis, treatment planning, and patient outcomes. Despite increased interest in capturing SDoH in electronic health records (EHRs), such information is typically locked in unstructured clinical notes. Natural language processing (NLP) is the key technology to extract SDoH information from clinical text and expand its utility in patient care and research. This article presents a systematic review of the state-of-the-art NLP approaches and tools that focus on identifying and extracting SDoH data from unstructured clinical text in EHRs.

MATERIALS AND METHODS

A broad literature search was conducted in February 2021 using 3 scholarly databases (ACL Anthology, PubMed, and Scopus) following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 6402 publications were initially identified, and after applying the study inclusion criteria, 82 publications were selected for the final review.

RESULTS

Smoking status (n = 27), substance use (n = 21), homelessness (n = 20), and alcohol use (n = 15) are the most frequently studied SDoH categories. Homelessness (n = 7) and other less-studied SDoH (eg, education, financial problems, social isolation and support, family problems) are mostly identified using rule-based approaches. In contrast, machine learning approaches are popular for identifying smoking status (n = 13), substance use (n = 9), and alcohol use (n = 9).

CONCLUSION

NLP offers significant potential to extract SDoH data from narrative clinical notes, which in turn can aid in the development of screening tools, risk prediction models, and clinical decision support systems.

Emotional Responses and Perceived Relative Harm Mediate the Effect of Exposure to Misinformation about E-Cigarettes on Twitter and Intention to Purchase E-Cigarettes among Adult Smokers

There is a gap in knowledge on the affective mechanisms underlying effects of exposure to health misinformation. This study aimed to understand whether discrete emotional responses and perceived relative harm of e-cigarettes versus smoking mediate the effect of exposure to tweets about the harms of e-cigarettes on Twitter and intention to purchase e-cigarettes among adult smokers. We conducted a web-based experiment in November 2019 among 2400 adult smokers who were randomly assigned to view one of four conditions of tweets containing different levels of misinformation. We fitted mediation models using structural equation modeling and bootstrap procedures to assess the indirect effects of exposure to tweets through perceived relative harm of e-cigarettes and six discrete emotions. Our findings support that exposure to tweets about harms of e-cigarettes influence intention to purchase e-cigarettes through perceived relative harm, discrete emotional responses, and serially through emotional responses and perceived relative harm. Feeling worried, hopeful, and happy mediated the effects of condition on intention to purchase e-cigarettes. Feeling scared, worried, angry, and hopeful mediated the effects serially through perceived relative harm. Affective responses and perceived relative harm following exposure to misinformation about e-cigarette harm may mediate the relationship with intention to purchase e-cigarettes among adult smokers.

Search for Active-Sterile Antineutrino Mixing Using Neutral-Current Interactions with the NOvA Experiment

This Letter reports results from the first long-baseline search for sterile antineutrinos mixing in an accelerator-based antineutrino-dominated beam. The rate of neutral-current interactions in the two NOvA detectors, at distances of 1 and 810 km from the beam source, is analyzed using an exposure of 12.51×10^{20} protons-on-target from the NuMI beam at Fermilab running in antineutrino mode. A total of 121 of neutral-current candidates are observed at the far detector, compared to a prediction of 122±11(stat.)±15(syst.) assuming mixing only between three active flavors. No evidence for ν[over ¯]_{μ}→ν[over ¯]_{s} oscillation is observed. Interpreting this result within a 3+1 model, constraints are placed on the mixing angles θ_{24}<25° and θ_{34}<32° at the 90% C.L. for 0.05  eV^{2}≤Δm_{41}^{2}≤0.5  eV^{2}, the range of mass splittings that produces no significant oscillations at the near detector. These are the first 3+1 confidence limits set using long-baseline accelerator antineutrinos.

MicroRNA-21 Regulates Diametrically Opposed Biological Functions of Regulatory T Cells

Regulatory T cells (Tregs) are considered important for controlling the onset and development of autoimmune disease. Although studies have shown that miR-21 is expressed at higher levels in Treg cells, it remains largely elusive whether miR-21 regulates the immune-suppressive function of Tregs. In the current study, we generated mice lacking miR-21 specifically in their Tregs and investigated the role of miR-21 in regulating Treg function both in vitro and in vivo. Our study revealed that Tregs lacking miR-21 exhibit normal phenotype and unaltered function in suppressing T cell proliferation and dendritic cell activation in vitro. However, compared with miR-21-sufficient Tregs, they produce significant more IL-17 and IL-10 when under pathogenic Th17-priming condition. Adenoviral delivery of miR-21 into Treg cells is able to reduce the expression of both IL-17 and IL-10. Mechanistic study revealed that miR-21 down-regulates IL-10 expression through direct targeting of IL-10, and suppresses reprogramming of Tregs into IL-17-secreting cells through down-regulating Stat3 activity. However, we detected no significant or marginal difference in the development of various autoimmune diseases between wild type mice and mice with Treg-specific deletion of miR-21. In conclusion, our study demonstrated that miR-21 in Tregs regulates diametrically opposed biological Treg functions and is largely dispensable for the development of autoimmune disease.

The OneFlorida Data Trust: a centralized, translational research data infrastructure of statewide scope

The OneFlorida Data Trust is a centralized research patient data repository created and managed by the OneFlorida Clinical Research Consortium ("OneFlorida"). It comprises structured electronic health record (EHR), administrative claims, tumor registry, death, and other data on 17.2 million individuals who received healthcare in Florida between January 2012 and the present. Ten healthcare systems in Miami, Orlando, Tampa, Jacksonville, Tallahassee, Gainesville, and rural areas of Florida contribute EHR data, covering the major metropolitan regions in Florida. Deduplication of patients is accomplished via privacy-preserving entity resolution (precision 0.97-0.99, recall 0.75), thereby linking patients' EHR, claims, and death data. Another unique feature is the establishment of mother-baby relationships via Florida vital statistics data. Research usage has been significant, including major studies launched in the National Patient-Centered Clinical Research Network ("PCORnet"), where OneFlorida is 1 of 9 clinical research networks. The Data Trust's robust, centralized, statewide data are a valuable and relatively unique research resource.

Deep learning models in detection of dietary supplement adverse event signals from Twitter

Objective

The objective of this study is to develop a deep learning pipeline to detect signals on dietary supplement-related adverse events (DS AEs) from Twitter.

Materials and Methods

We obtained 247 807 tweets ranging from 2012 to 2018 that mentioned both DS and AE. We designed a tailor-made annotation guideline for DS AEs and annotated biomedical entities and relations on 2000 tweets. For the concept extraction task, we fine-tuned and compared the performance of BioClinical-BERT, PubMedBERT, ELECTRA, RoBERTa, and DeBERTa models with a CRF classifier. For the relation extraction task, we fine-tuned and compared BERT models to BioClinical-BERT, PubMedBERT, RoBERTa, and DeBERTa models. We chose the best-performing models in each task to assemble an end-to-end deep learning pipeline to detect DS AE signals and compared the results to the known DS AEs from a DS knowledge base (ie, iDISK).

Results

DeBERTa-CRF model outperformed other models in the concept extraction task, scoring a lenient microaveraged F1 score of 0.866. RoBERTa model outperformed other models in the relation extraction task, scoring a lenient microaveraged F1 score of 0.788. The end-to-end pipeline built on these 2 models was able to extract DS indication and DS AEs with a lenient microaveraged F1 score of 0.666.

Conclusion

We have developed a deep learning pipeline that can detect DS AE signals from Twitter. We have found DS AEs that were not recorded in an existing knowledge base (iDISK) and our proposed pipeline can as sist DS AE pharmacovigilance.

Disparities in Pediatric Patient Portal Activation and Feature Use

Objective

Disparities in adult patient portal adoption are well-documented; however, less is known about disparities in portal adoption in pediatrics. This study examines the prevalence and factors associated with patient portal activation and the use of specific portal features in general pediatrics.

Materials and methods

We analyzed electronic health record data from 2012 to 2020 in a large academic medical center that offers both parent and adolescent portals. We summarized portal activation and use of select portal features (messaging, records access and management, appointment management, visit/admissions summaries, and interactive feature use). We used logistic regression to model factors associated with patient portal activation among all patients along with feature use and frequent feature use among ever users (ie, ≥1 portal use).

Results

Among 52 713 unique patients, 39% had activated the patient portal, including 36% of patients aged 0–11, 41% of patients aged 12–17, and 62% of patients aged 18–21 years. Among activated accounts, ever use of specific features ranged from 28% for visit/admission summaries to 92% for records access and management. Adjusted analyses showed patients with activated accounts were more likely to be adolescents or young adults, white, female, privately insured, and less socioeconomically vulnerable. Individual feature use among ever users generally followed the same pattern.

Conclusions

Our findings demonstrate that important disparities persist in portal adoption in pediatric populations, highlighting the need for strategies to promote equitable access to patient portals.

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)

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 p < .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.

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

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.

Optimizing Identification of People Living with HIV from Electronic Medical Records: Computable Phenotype Development and Validation

BACKGROUND

Electronic health record (EHR)-based computable phenotype algorithms allow researchers to efficiently identify a large virtual cohort of Human Immunodeficiency Virus (HIV) patients. Built upon existing algorithms, we refined, improved, and validated an HIV phenotype algorithm using data from the OneFlorida Data Trust, a repository of linked claims data and EHRs from its clinical partners, which provide care to over 15 million patients across all 67 counties in Florida.

METHODS

Our computable phenotype examined information from multiple EHR domains, including clinical encounters with diagnoses, prescription medications, and laboratory tests. To identify an HIV case, the algorithm requires the patient to have at least one diagnostic code for HIV and meet one of the following criteria: have 1+ positive HIV laboratory, have been prescribed with HIV medications, or have 3+ visits with HIV diagnostic codes. The computable phenotype was validated against a subset of clinical notes.

RESULTS

Among the 15+ million patients from OneFlorida, we identified 61,313 patients with confirmed HIV diagnosis. Among them, 8.05% met all four inclusion criteria, 69.7% met the 3+ HIV encounters criteria in addition to having HIV diagnostic code, and 8.1% met all criteria except for having positive laboratories. Our algorithm achieved higher sensitivity (98.9%) and comparable specificity (97.6%) relative to existing algorithms (77-83% sensitivity, 86-100% specificity). The mean age of the sample was 42.7 years, 58% male, and about half were Black African American. Patients' average follow-up period (the time between the first and last encounter in the EHRs) was approximately 4.6 years. The median number of all encounters and HIV-related encounters were 79 and 21, respectively.

CONCLUSION

By leveraging EHR data from multiple clinical partners and domains, with a considerably diverse population, our algorithm allows more flexible criteria for identifying patients with incomplete laboratory test results and medication prescribing history compared with prior studies.

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

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.

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

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.

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

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.

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

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.

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

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.

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

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.

Associations between the chemical composition of PM2.5 and gestational diabetes mellitus

BACKGROUND

Fine particulate matter (PM_2.5) is a complex mixture of fine particulates with large spatiotemporal heterogeneities in chemical compositions. While PM_2.5 has been associated with gestational diabetes mellitus (GDM), little is known about the relationship between specific chemical components of PM_2.5 and GDM. We examined the associations between GDM and pregnancy exposures to PM_2.5 and its compositions, including sulfate (SO₄^2-), ammonium (NH₄⁺), nitrate (NO₃^-), organic matter (OM), black carbon (BC), mineral dust (DUST), and sea-salt (SS), and to identify critical windows of exposure.

METHODS

We used data from the 2005-2015 Florida Vital Statistics Birth Records. A well-validated geoscience-derived model was used to estimate women's pregnancy exposures to PM_2.5 and its compositions. Distributed lag models were used to examine the associations and to identify the critical windows of exposure.

RESULTS

A total of 2,078,669 women were included. In single-pollutant models, after controlling for potential confounders, positive associations between PM_2.5 and GDM were observed during the second trimester of pregnancy. We found positive associations between SO₄^2-, NH₄⁺, NO₃^-, OM and BC, with largest effect sizes observed in the 21-24 weeks of pregnancy. Negative associations were observed for DUST and SS. Consistent results for NH₄⁺, OM, DUST and SS were observed in the multi-pollutant models.

CONCLUSIONS

Exposures to PM_2.5 and its compositions (mainly NH₄⁺, OM) during the second trimester are positively associated with GDM, especially for exposures during the 21-24 weeks of pregnancy. Further studies are needed to confirm the findings and examine the underlying mechanisms.

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

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.

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

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.

Semantic standards of external exposome data

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.

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

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 (n = 36,615) of patients with confirmed diagnosis of coronavirus disease 2019 were included. Twelve percent (n = 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%; χ2[1] = 3,266.08, p < 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.

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

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 4-3 (IC₅₀ = 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, 4-3 exhibited decent pharmacokinetic properties in vivo 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.

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

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.