A Robust Biomimetic Superhydrophobic Coating with Superior Mechanical Durability and Chemical Stability for Inner Pipeline Protection

Durable superhydrophobic anti-erosion/anticorrosion coatings are highly demanded across various applications. However, achieving coatings with exceptional superhydrophobicity, mechanical strength, and corrosion resistance remains a grand challenge. Herein, a robust microstructure coating, inspired by the cylindrical structures situated on the surface of conch shell, for mitigating erosion and corrosion damages in gas transportation pipelines is reported. Specifically, citric acid monohydrate as a pore-forming agent is leveraged to create a porous structure between layers, effectively buffering the impact on the surface. As a result, the coating demonstrates remarkable wear resistance and water repellency. Importantly, even after abrasion by sandpaper and an erosion loop test, the resulting superhydrophobic surfaces retain the water repellency. The design strategy offers a promising route to manufacturing multifunctional materials with desired features and structural complexities, thereby enabling effective self-cleaning and antifouling abilities in harsh operating environments for an array of applications, including self-cleaning windows, antifouling coatings for medical devices, and anti-erosion/anticorrosion protection, among other areas.

Quaternary ammonium-tethered hyperbranched polyurea nanoassembly synergized with antibiotics for enhanced antimicrobial efficacy

The effective transportation of antibiotics to bacteria embedded within a biofilm consisting of a dense matrix of extracellular polymeric substances is still a challenge in the treatment of bacterial biofilm associated infections. Here, we developed an antibiotic nanocarrier constructed from quaternary ammonium-tethered hyperbranched polyureas (HPUs-QA), which showed high loading capacity for a model antibiotic, rifampicin, and high efficacy in the transportation of rifampicin to biofilms. The rifampicin-loaded HPUs-QA nanoassembly (HPUs-Rif/QA) demonstrated a synergistic antimicrobial effect in killing planktonic bacteria and eradicating the corresponding biofilms. Compared to the treatment of bacteria-infected chronic wounds by either HPUs-QA or rifampicin alone, HPUs-Rif/QA showed superior efficacy in promoting wound healing by more effectively inhibiting bacteria colonization. This study highlights the potential of the HPUs-QA nanoassembly in synergistic action with antibiotics for the treatment of biofilm associated infections.

Feasibility of Identifying Factors Related to Alzheimer's Disease and Related Dementia in Real-World Data

A comprehensive view of factors associated with AD/ADRD will significantly aid in studies to develop new treatments for AD/ADRD and identify high-risk populations and patients for prevention efforts. In our study, we summarized the risk factors for AD/ADRD by reviewing existing meta-analyses and review articles on risk and preventive factors for AD/ADRD. In total, we extracted 477 risk factors in 10 categories from 537 studies. We constructed an interactive knowledge map to disseminate our study results. Most of the risk factors are accessible from structured Electronic Health Records (EHRs), and clinical narratives show promise as information sources. However, evaluating genomic risk factors using RWD remains a challenge, as genetic testing for AD/ADRD is still not a common practice and is poorly documented in both structured and unstructured EHRs. Considering the constantly evolving research on AD/ADRD risk factors, literature mining via NLP methods offers a solution to automatically update our knowledge map.

Develop and Validate a Computable Phenotype for the Identification of Alzheimer's Disease Patients Using Electronic Health Record Data

INTRODUCTION

Alzheimer's Disease (AD) are often misclassified in electronic health records (EHRs) when relying solely on diagnostic codes. This study aims to develop a more accurate, computable phenotype (CP) for identifying AD patients by using both structured and unstructured EHR data.

METHODS

We used EHRs from the University of Florida Health (UF Health) system and created rule-based CPs iteratively through manual chart reviews. The CPs were then validated using data from the University of Texas Health Science Center at Houston (UT Health) and the University of Minnesota (UMN).

RESULTS

Our best-performing CP is " patient has at least 2 AD diagnoses and AD-related keywords " with an F1-score of 0.817 at UF, and 0.961 and 0.623 at UT Health and UMN, respectively.

DISCUSSION

We developed and validated rule-based CPs for AD identification with good performance, crucial for studies that aim to use real-world data like EHRs.

Dynamic Prognosis Prediction for Patients on DAPT After Drug-Eluting Stent Implantation: Model Development and Validation

BACKGROUND

The rapid evolution of artificial intelligence (AI) in conjunction with recent updates in dual antiplatelet therapy (DAPT) management guidelines emphasizes the necessity for innovative models to predict ischemic or bleeding events after drug-eluting stent implantation. Leveraging AI for dynamic prediction has the potential to revolutionize risk stratification and provide personalized decision support for DAPT management.

METHODS AND RESULTS

We developed and validated a new AI-based pipeline using retrospective data of drug-eluting stent-treated patients, sourced from the Cerner Health Facts data set (n=98 236) and Optum's de-identified Clinformatics Data Mart Database (n=9978). The 36 months following drug-eluting stent implantation were designated as our primary forecasting interval, further segmented into 6 sequential prediction windows. We evaluated 5 distinct AI algorithms for their precision in predicting ischemic and bleeding risks. Model discriminative accuracy was assessed using the area under the receiver operating characteristic curve, among other metrics. The weighted light gradient boosting machine stood out as the preeminent model, thus earning its place as our AI-DAPT model. The AI-DAPT demonstrated peak accuracy in the 30 to 36 months window, charting an area under the receiver operating characteristic curve of 90% [95% CI, 88%-92%] for ischemia and 84% [95% CI, 82%-87%] for bleeding predictions.

CONCLUSIONS

Our AI-DAPT excels in formulating iterative, refined dynamic predictions by assimilating ongoing updates from patients' clinical profiles, holding value as a novel smart clinical tool to facilitate optimal DAPT duration management with high accuracy and adaptability.

Genetic Testing for Cancer Risk and Perceived Importance of Genetic Information Among US Population by Race and Ethnicity: a Cross-sectional Study

BACKGROUND

Genetic testing can help determine the risk of many cancers and guide cancer prevention and treatment plans. Despite increasing concern about disparities in precision cancer medicine, public knowledge and cancer genetic testing by race and ethnicity have not been well investigated.

METHODS

We analyzed data from the 2020 Health Information National Trends Survey in 2022. Self-reported cancer genetic testing (e.g., Lynch syndrome, BRCA1/2) knowledge and utilization were compared by race and ethnicity. Perceived importance of genetic information for cancer care (prevention, detection, and treatment) was also examined in relation to the uptake of cancer genetic testing. Multivariable logistic regression models were employed to examine factors associated with knowledge and genetic testing to calculate predicted probability of undergoing genetic testing by race and ethnicity.

RESULTS

Of 3551 study participants, 37.8% reported having heard of genetic testing for cancer risk and 3.9% stated that they underwent cancer genetic testing. Being non-Hispanic Black (OR=0.47, 95% CI=0.30-0.75) or Hispanic (OR=0.56, CI=0.35-0.90) was associated with lower odds of genetic testing knowledge. Although Hispanic or non-Hispanic Black respondents were more likely to perceive higher importance of genetic information versus non-Hispanic Whites, they had a lower predicted probability of cancer genetic testing.

CONCLUSION

Non-Hispanic Black and Hispanic adults had lower knowledge and were less likely to undergo cancer genetic testing than non-Hispanic Whites. Further research is needed on sources of genetic testing information for racial and ethnic minorities and the barriers to accessing genetic testing to inform the development of effective cancer risk genetic testing promotion.

Heterogeneous treatment effects of metformin on risk of dementia in patients with type 2 diabetes: A longitudinal observational study

INTRODUCTION

Little is known about the heterogeneous treatment effects of metformin on dementia risk in people with type 2 diabetes (T2D).

METHODS

Participants (≥ 50 years) with T2D and normal cognition at baseline were identified from the National Alzheimer's Coordinating Center database (2005-2021). We applied a doubly robust learning approach to estimate risk differences (RD) with a 95% confidence interval (CI) for dementia risk between metformin use and no use in the overall population and subgroups identified through a decision tree model.

RESULTS

Among 1393 participants, 104 developed dementia over a 4-year median follow-up. Metformin was significantly associated with a lower risk of dementia in the overall population (RD, -3.2%; 95% CI, -6.2% to -0.2%). We identified four subgroups with varied risks for dementia, defined by neuropsychiatric disorders, non-steroidal anti-inflammatory drugs, and antidepressant use.

DISCUSSION

Metformin use was significantly associated with a lower risk of dementia in individuals with T2D, with significant variability among subgroups.

Real-World Effectiveness of BNT162b2 Against Infection and Severe Diseases in Children and Adolescents

BACKGROUND

The efficacy of the BNT162b2 vaccine in pediatrics was assessed by randomized trials before the Omicron variant's emergence. The long-term durability of vaccine protection in this population during the Omicron period remains limited.

OBJECTIVE

To assess the effectiveness of BNT162b2 in preventing infection and severe diseases with various strains of the SARS-CoV-2 virus in previously uninfected children and adolescents.

DESIGN

Comparative effectiveness research accounting for underreported vaccination in 3 study cohorts: adolescents (12 to 20 years) during the Delta phase and children (5 to 11 years) and adolescents (12 to 20 years) during the Omicron phase.

SETTING

A national collaboration of pediatric health systems (PEDSnet).

PARTICIPANTS

77 392 adolescents (45 007 vaccinated) during the Delta phase and 111 539 children (50 398 vaccinated) and 56 080 adolescents (21 180 vaccinated) during the Omicron phase.

INTERVENTION

First dose of the BNT162b2 vaccine versus no receipt of COVID-19 vaccine.

MEASUREMENTS

Outcomes of interest include documented infection, COVID-19 illness severity, admission to an intensive care unit (ICU), and cardiac complications. The effectiveness was reported as (1-relative risk)*100, with confounders balanced via propensity score stratification.

RESULTS

During the Delta period, the estimated effectiveness of the BNT162b2 vaccine was 98.4% (95% CI, 98.1% to 98.7%) against documented infection among adolescents, with no statistically significant waning after receipt of the first dose. An analysis of cardiac complications did not suggest a statistically significant difference between vaccinated and unvaccinated groups. During the Omicron period, the effectiveness against documented infection among children was estimated to be 74.3% (CI, 72.2% to 76.2%). Higher levels of effectiveness were seen against moderate or severe COVID-19 (75.5% [CI, 69.0% to 81.0%]) and ICU admission with COVID-19 (84.9% [CI, 64.8% to 93.5%]). Among adolescents, the effectiveness against documented Omicron infection was 85.5% (CI, 83.8% to 87.1%), with 84.8% (CI, 77.3% to 89.9%) against moderate or severe COVID-19, and 91.5% (CI, 69.5% to 97.6%) against ICU admission with COVID-19. The effectiveness of the BNT162b2 vaccine against the Omicron variant declined 4 months after the first dose and then stabilized. The analysis showed a lower risk for cardiac complications in the vaccinated group during the Omicron variant period.

LIMITATION

Observational study design and potentially undocumented infection.

CONCLUSION

This study suggests that BNT162b2 was effective for various COVID-19-related outcomes in children and adolescents during the Delta and Omicron periods, and there is some evidence of waning effectiveness over time.

PRIMARY FUNDING SOURCE

National Institutes of Health.

Geographic variation and racial disparities in adoption of newer glucose-lowering drugs with cardiovascular benefits among US Medicare beneficiaries with type 2 diabetes

BACKGROUND

Prior studies have shown disparities in the uptake of cardioprotective newer glucose-lowering drugs (GLDs), including sodium-glucose cotranwsporter-2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP1a). This study aimed to characterize geographic variation in the initiation of newer GLDs and the geographic variation in the disparities in initiating these medications.

METHODS

Using 2017-2018 claims data from a 15% random nationwide sample of Medicare Part D beneficiaries, we identified individuals diagnosed with type 2 diabetes (T2D), who had ≥1 GLD prescriptions, and did not use SGLT2i or GLP1a in the year prior to the index date,1/1/2018. Patients were followed up for a year. The cohort was spatiotemporally linked to Dartmouth hospital-referral regions (HRRs), with each patient assigned to 1 of 306 HRRs. We performed multivariable Poisson regression to estimate adjusted initiation rates, and multivariable logistic regression to assess racial disparities in each HRR.

RESULTS

Among 795,469 individuals with T2D included in the analyses, the mean (SD) age was 73 (10) y, 53.3% were women, 12.2% were non-Hispanic Black, and 7.2% initiated a newer GLD in the follow-up year. In the adjusted model including clinical factors, compared to non-Hispanic White patients, non-Hispanic Black (initiation rate ratio, IRR [95% CI]: 0.66 [0.64-0.68]), American Indian/Alaska Native (0.74 [0.66-0.82]), Hispanic (0.85 [0.82-0.87]), and Asian/Pacific islander (0.94 [0.89-0.98]) patients were less likely to initiate newer GLDs. Significant geographic variation was observed across HRRs, with an initiation rate spanning 2.7%-13.6%.

CONCLUSIONS

This study uncovered substantial geographic variation and the racial disparities in initiating newer GLDs.

Real-World Effectiveness of Lung Cancer Screening Using Deep Learning-Based Counterfactual Prediction

The benefits and harms of lung cancer screening (LCS) for patients in the real-world clinical setting have been argued. Recently, discriminative prediction modeling of lung cancer with stratified risk factors has been developed to investigate the real-world effectiveness of LCS from observational data. However, most of these studies were conducted at the population level that only measured the difference in the average outcome between groups. In this study, we built counterfactual prediction models for lung cancer risk and mortality and examined for individual patients whether LCS as a hypothetical intervention reduces lung cancer risk and subsequent mortality. We investigated traditional and deep learning (DL)-based causal methods that provide individualized treatment effect (ITE) at the patient level and evaluated them with a cohort from the OneFlorida+ Clinical Research Consortium. We further discussed and demonstrated that the ITE estimation model can be used to personalize clinical decision support for a broader population.

Morphological Profiling for Drug Discovery in the Era of Deep Learning

Morphological profiling is a valuable tool in phenotypic drug discovery. The advent of high-throughput automated imaging has enabled the capturing of a wide range of morphological features of cells or organisms in response to perturbations at the single-cell resolution. Concurrently, significant advances in machine learning and deep learning, especially in computer vision, have led to substantial improvements in analyzing large-scale high-content images at high-throughput. These efforts have facilitated understanding of compound mechanism-of-action (MOA), drug repurposing, characterization of cell morphodynamics under perturbation, and ultimately contributing to the development of novel therapeutics. In this review, we provide a comprehensive overview of the recent advances in the field of morphological profiling. We summarize the image profiling analysis workflow, survey a broad spectrum of analysis strategies encompassing feature engineering- and deep learning-based approaches, and introduce publicly available benchmark datasets. We place a particular emphasis on the application of deep learning in this pipeline, covering cell segmentation, image representation learning, and multimodal learning. Additionally, we illuminate the application of morphological profiling in phenotypic drug discovery and highlight potential challenges and opportunities in this field.

A Computable Phenotype for the Identification of Sexual and Gender Minorities in Electronic Health Records

Sexual gender minorities, including lesbian, gay, and bisexual (LGB) individuals face unique challenges due to discrimination, stigma, and marginalization, which negatively impact their well-being. Electronic health record (EHR) systems present an opportunity for LGB research, but accurately identifying LGB individuals in EHRs is challenging. Our study developed and validated a rule-based computable phenotype (CP) to identify LGB individuals and their subgroups using both structured data and unstructured clinical narratives from a large integrated health system. Validating against a sample of 537 chart-reviewed patients, our three best performing CP algorithms balancing different performance metrics, each achieved sensitivity of 1.000, PPV of 0.982, and F1-score of 0.875 in identifying LGB individuals, respectively. Applying the three best-performing CPs, our study also found that the LGB population is younger and experiences a disproportionate burden of adverse health outcomes, particularly mental health distress.

Identification of Outcome-Oriented Progression Subtypes from Mild Cognitive Impairment to Alzheimer's Disease Using Electronic Health Records

Alzheimer's disease (AD) is a complex heterogeneous neurodegenerative disease that requires an in-depth understanding of its progression pathways and contributing factors to develop effective risk stratification and prevention strategies. In this study, we proposed an outcome-oriented model to identify progression pathways from mild cognitive impairment (MCI) to AD using electronic health records (EHRs) from the OneFlorida+ Clinical Research Consortium. To achieve this, we employed the long short-term memory (LSTM) network to extract relevant information from the sequential records of each patient. The hierarchical agglomerative clustering was then applied to the learned representation to group patients based on their progression subtypes. Our approach identified multiple progression pathways, each of which represented distinct patterns of disease progression from MCI to AD. These pathways can serve as a valuable resource for researchers to understand the factors influencing AD progression and to develop personalized interventions to delay or prevent the onset of the disease.

Identifying demographic and home features influencing older adults' home functioning by using machine learning models

OBJECTIVES

This study aimed to identify demographic and housing features associated with functional difficulties experienced by older adults in their homes.

PARTICIPANTS

Individuals aged ≥ 65 years who completed American Housing Survey (AHS) questionnaires. We selected one random person per household and excluded participants with missing data for the 12 AHS functional challenge items.

METHODS

Multiple machine learning models were compared to identify the best-performing model, which was then used to analyze the impact of demographic and housing features on older adults' functional difficulties at home.

RESULTS

The random forest model was selected for its preferred predictive performance (accuracy: 85.8%, sensitivity: 94.4%, specificity: 60.2%, precision: 87.6%, and negative predictive value: 78.2%). The top five variables that significantly influenced the model were: 1) walking disability, 2) presence or use of a cane or walker, 3) presence or use of handrails or grab bars in the bathroom, 4) go-outside-home disability, and 5) self-care disability. These variables had a stronger impact on the model than the householder's health and age.

CONCLUSION

Home modifications and environmental adaptations may be critical in enhancing functional abilities and independence among older adults. These findings could inform the development of interventions that promote safe and accessible living environments for older adults, thereby improving their quality of life.

Patient-Provider Discussion About Lung Cancer Screening by Race and Ethnicity: Implications for Equitable Uptake of Lung Cancer Screening

BACKGROUND

Physician-patient discussions regarding lung cancer screening (LCS) are uncommon and its racial and ethnic disparities are under-investigated. We examined the racial and ethnic disparities in the trends and frequency of LCS discussion among the LCS-eligible United States (US) population.

METHODS

We analyzed data from the Health Information National Trends Survey from 2014 to 2020. LCS-eligible individuals were defined as adults aged 55 to 80 years old who have a current or former smoking history. We estimated the trends and frequency of LCS discussions and adjusted the probability of having an LCS discussion by racial and ethnic groups.

RESULTS

Among 2136 LCS-eligible participants (representing 22.7 million US adults), 12.9% (95% CI, 10.9%-15%) reported discussing LCS with their providers in the past year. The frequency of LCS discussion was lowest among non-Hispanic White participants (12.3%, 95% CI, 9.9%-14.7%) compared to other racial and ethnic groups (14.1% in Hispanic to 15.3% in non-Hispanic Black). A significant increase over time was only observed among non-Hispanic Black participants (10.1% in 2014 to 22.1% in 2020; P = .05) and non-Hispanic Whites (8.5% in 2014 to 14% in 2020; P = .02). In adjusted analyses, non-Hispanic Black participants (14.6%, 95% CI, 12.3%-16.7%) had a significantly higher probability of LCS discussion than non-Hispanic Whites (12.1%, 95% CI, 11.4%-12.7%).

CONCLUSION

Patient-provider LCS discussion was uncommon in the LCS-eligible US population. Non-Hispanic Black individuals were more likely to have LCS discussions than other racial and ethnic groups. There is a need for more research to clarify the discordance between LCS discussions and the actual screening uptake in this population.

Quantifying Health Outcome Disparity in Invasive Methicillin-Resistant Staphylococcus aureus Infection using Fairness Algorithms on Real-World Data

This study quantifies health outcome disparities in invasive Methicillin-Resistant Staphylococcus aureus (MRSA) infections by leveraging a novel artificial intelligence (AI) fairness algorithm, the Fairness-Aware Causal paThs (FACTS) decomposition, and applying it to real-world electronic health record (EHR) data. We spatiotemporally linked 9 years of EHRs from a large healthcare provider in Florida, USA, with contextual social determinants of health (SDoH). We first created a causal structure graph connecting SDoH with individual clinical measurements before/upon diagnosis of invasive MRSA infection, treatments, side effects, and outcomes; then, we applied FACTS to quantify outcome potential disparities of different causal pathways including SDoH, clinical and demographic variables. We found moderate disparity with respect to demographics and SDoH, and all the top ranked pathways that led to outcome disparities in age, gender, race, and income, included comorbidity. Prior kidney impairment, vancomycin use, and timing were associated with racial disparity, while income, rurality, and available healthcare facilities contributed to gender disparity. From an intervention standpoint, our results highlight the necessity of devising policies that consider both clinical factors and SDoH. In conclusion, this work demonstrates a practical utility of fairness AI methods in public health settings.

Predicting preterm birth using explainable machine learning in a prospective cohort of nulliparous and multiparous pregnant women

Preterm birth (PTB) presents a complex challenge in pregnancy, often leading to significant perinatal and long-term morbidities. "While machine learning (ML) algorithms have shown promise in PTB prediction, the lack of interpretability in existing models hinders their clinical utility. This study aimed to predict PTB in a pregnant population using ML models, identify the key risk factors associated with PTB through the SHapley Additive exPlanations (SHAP) algorithm, and provide comprehensive explanations for these predictions to assist clinicians in providing appropriate care. This study analyzed a dataset of 3509 pregnant women in the United Arab Emirates and selected 35 risk factors associated with PTB based on the existing medical and artificial intelligence literature. Six ML algorithms were tested, wherein the XGBoost model exhibited the best performance, with an area under the operator receiving curves of 0.735 and 0.723 for parous and nulliparous women, respectively. The SHAP feature attribution framework was employed to identify the most significant risk factors linked to PTB. Additionally, individual patient analysis was performed using the SHAP and the local interpretable model-agnostic explanation algorithms (LIME). The overall incidence of PTB was 11.23% (11 and 12.1% in parous and nulliparous women, respectively). The main risk factors associated with PTB in parous women are previous PTB, previous cesarean section, preeclampsia during pregnancy, and maternal age. In nulliparous women, body mass index at delivery, maternal age, and the presence of amniotic infection were the most relevant risk factors. The trained ML prediction model developed in this study holds promise as a valuable screening tool for predicting PTB within this specific population. Furthermore, SHAP and LIME analyses can assist clinicians in understanding the individualized impact of each risk factor on their patients and provide appropriate care to reduce morbidity and mortality related to PTB.

The role of health system penetration rate in estimating the prevalence of type 1 diabetes in children and adolescents using electronic health records

OBJECTIVE

Having sufficient population coverage from the electronic health records (EHRs)-connected health system is essential for building a comprehensive EHR-based diabetes surveillance system. This study aimed to establish an EHR-based type 1 diabetes (T1D) surveillance system for children and adolescents across racial and ethnic groups by identifying the minimum population coverage from EHR-connected health systems to accurately estimate T1D prevalence.

MATERIALS AND METHODS

We conducted a retrospective, cross-sectional analysis involving children and adolescents <20 years old identified from the OneFlorida+ Clinical Research Network (2018-2020). T1D cases were identified using a previously validated computable phenotyping algorithm. The T1D prevalence for each ZIP Code Tabulation Area (ZCTA, 5 digits), defined as the number of T1D cases divided by the total number of residents in the corresponding ZCTA, was calculated. Population coverage for each ZCTA was measured using observed health system penetration rates (HSPR), which was calculated as the ratio of residents in the corresponding ZTCA and captured by OneFlorida+ to the overall population in the same ZCTA reported by the Census. We used a recursive partitioning algorithm to identify the minimum required observed HSPR to estimate T1D prevalence and compare our estimate with the reported T1D prevalence from the SEARCH study.

RESULTS

Observed HSPRs of 55%, 55%, and 60% were identified as the minimum thresholds for the non-Hispanic White, non-Hispanic Black, and Hispanic populations. The estimated T1D prevalence for non-Hispanic White and non-Hispanic Black were 2.87 and 2.29 per 1000 youth, which are comparable to the reference study's estimation. The estimated prevalence of T1D for Hispanics (2.76 per 1000 youth) was higher than the reference study's estimation (1.48-1.64 per 1000 youth). The standardized T1D prevalence in the overall Florida population was 2.81 per 1000 youth in 2019.

CONCLUSION

Our study provides a method to estimate T1D prevalence in children and adolescents using EHRs and reports the estimated HSPRs and prevalence of T1D for different race and ethnicity groups to facilitate EHR-based diabetes surveillance.

High-throughput target trial emulation for Alzheimer's disease drug repurposing with real-world data

Target trial emulation is the process of mimicking target randomized trials using real-world data, where effective confounding control for unbiased treatment effect estimation remains a main challenge. Although various approaches have been proposed for this challenge, a systematic evaluation is still lacking. Here we emulated trials for thousands of medications from two large-scale real-world data warehouses, covering over 10 years of clinical records for over 170 million patients, aiming to identify new indications of approved drugs for Alzheimer's disease. We assessed different propensity score models under the inverse probability of treatment weighting framework and suggested a model selection strategy for improved baseline covariate balancing. We also found that the deep learning-based propensity score model did not necessarily outperform logistic regression-based methods in covariate balancing. Finally, we highlighted five top-ranked drugs (pantoprazole, gabapentin, atorvastatin, fluticasone, and omeprazole) originally intended for other indications with potential benefits for Alzheimer's patients.

A Fair Individualized Polysocial Risk Score for Identifying Increased Social Risk in Type 2 Diabetes

Background

Racial and ethnic minority groups and individuals facing social disadvantages, which often stem from their social determinants of health (SDoH), bear a disproportionate burden of type 2 diabetes (T2D) and its complications. It is crucial to implement effective social risk management strategies at the point of care.

Objective

To develop an electronic health records (EHR)-based machine learning (ML) analytical pipeline to address unmet social needs associated with hospitalization risk in patients with T2D.

Methods

We identified real-world patients with T2D from the EHR data from University of Florida (UF) Health Integrated Data Repository (IDR), incorporating both contextual SDoH (e.g., neighborhood deprivation) and individual-level SDoH (e.g., housing instability). The 2015-2020 data were used for training and validation and 2021-2022 data for independent testing. We developed a machine learning analytic pipeline, namely individualized polysocial risk score (iPsRS), to identify high social risk associated with hospitalizations in T2D patients, along with explainable AI (XAI) and fairness optimization.

Results

The study cohort included 10,192 real-world patients with T2D, with a mean age of 59 years and 58% female. Of the cohort, 50% were non-Hispanic White, 39% were non-Hispanic Black, 6% were Hispanic, and 5% were other races/ethnicities. Our iPsRS, including both contextual and individual-level SDoH as input factors, achieved a C statistic of 0.72 in predicting 1-year hospitalization after fairness optimization across racial and ethnic groups. The iPsRS showed excellent utility for capturing individuals at high hospitalization risk because of SDoH, that is, the actual 1-year hospitalization rate in the top 5% of iPsRS was 28.1%, ~13 times as high as the bottom decile (2.2% for 1-year hospitalization rate).

Conclusion

Our ML pipeline iPsRS can fairly and accurately screen for patients who have increased social risk leading to hospitalization in real word patients with T2D.

Unraveling Racial Disparities in Supportive Care Medication Use among End-of-Life Pancreatic Cancer Patients: Focus on Pain Management and Psychiatric Therapies

BACKGROUND

Supportive care medication use differences may contribute to racial disparities observed in health-related quality of life in patients with pancreatic cancer.

METHODS

In this observation study using the Surveillance, Epidemiology, and End Results-Medicare linked database, we sought to examine supportive care medication use disparities in patients with pancreatic cancer from 2005 to 2017 by race and ethnicity.

RESULTS

Among 74,309 patients included in the final analysis, racial and ethnic disparities in the use of supportive care medications were identified. After adjustment for confounding factors and compared with non-Hispanic Whites, minorities had significantly less use of opioids [Black: adjusted OR (aOR), 0.84; 95% confidence interval (CI), 0.79-0.88; Asian: aOR, 0.84; 95% CI, 0.79-0.90), and skeletomuscular relaxants (Black: aOR, 0.90; 95% CI, 0.82-0.99; Hispanic: aOR, 0.82; 95% CI, 0.74-0.91; Asian: aOR, 0.59; 95% CI, 0.51-0.68), and increased use of non-opioid analgesics (Hispanic: aOR, 1.16; 95% CI, 1.01-1.14; Asian: aOR, 1.37; 95% CI, 1.26-1.49). Racial and ethnic minorities had less use of antidepressants (Black: aOR, 0.56; 95% CI, 0.53-0.59; Hispanic: aOR, 0.77; 95% CI, 0.73-0.82; Asian: aOR, 0.47; 95% CI, 0.44-0.51), anxiolytics (Black: aOR, 0.78; 95% CI, 0.74-0.82; Hispanic: aOR, 0.66; 95% CI, 0.62-0.71; Asian: aOR, 0.52; 95% CI, 0.48-0.57), and antipsychotics (Hispanic: aOR, 0.90; 95% CI, 0.82-0.99; Asian: aOR, 0.84; 95% CI, 0.74-0.95).

CONCLUSIONS

Racial and ethnic disparities in the use of supportive care medications among patients with pancreatic cancer were observed, with the differences unexplained by sociodemographic factors.

IMPACT

Future studies should identify strategies to promote equitable use of supportive care medications among racial minorities and explore factors that may influence their use in these populations.

Numerical analysis of dispersion characteristics of underwater gas-oil two-phase leakage process

Fatigue failure, third-party destruction and internal corrosion may easily trigger gas and oil leakage during the operation of submarine multiphase pipelines. In order to analyze the underwater gas-oil plume development and migration law, a 3D model based on coupled Eulerian-Lagrangian numerical approach is proposed. The model is validated by laboratory experiment and the dynamic dispersion process of gas-oil plume in a large scale shallow sea environmental is further explored. Influencing factors such as leak location, leak size and water depth, flow pattern are investigated. The simulated results show that leak location affects the gas-oil plume migration behaviors by influencing the leakage amount. Water depth significantly affects gas-oil migration and the separation of gas plume and oil plume is gradually apparent as water depth increases. This study fills in the gap of ignoring the influence of flow pattern previously and is expected to help build more accurate emergency response guidelines.

Immune tolerance induced in the anterior chamber ameliorates corneal transplant rejection

The relevance of regulatory T cells (Tregs) in induction of tolerance against corneal allografts has been well established. However, whether Tregs can be induced in the anterior chamber and suppress local alloimmune response after corneal transplantation is largely unknown. In the current study we report that not only can alloantigen specific Tregs be generated in the anterior chamber during corneal transplantation, they also play important roles in suppressing allograft rejection. Allograft rejected mice exhibit reduced Treg induction in the anterior chamber and the ability of aqueous humor and corneal endothelial cells from allograft rejected mice to induce Tregs is compromised. Further analysis revealed that the expression of immune-tolerance-related molecules is significantly decreased. Finally, we demonstrate that increasing Treg cells specifically in the anterior chamber can effectively suppress allograft rejection and exhibits better efficacy in promoting corneal allograft survival than systemic administration of Treg cells. Our current study may provide new ideas for the prevention and treatment of corneal transplant rejection.

A study of generative large language model for medical research and healthcare

There are enormous enthusiasm and concerns in applying large language models (LLMs) to healthcare. Yet current assumptions are based on general-purpose LLMs such as ChatGPT, which are not developed for medical use. This study develops a generative clinical LLM, GatorTronGPT, using 277 billion words of text including (1) 82 billion words of clinical text from 126 clinical departments and approximately 2 million patients at the University of Florida Health and (2) 195 billion words of diverse general English text. We train GatorTronGPT using a GPT-3 architecture with up to 20 billion parameters and evaluate its utility for biomedical natural language processing (NLP) and healthcare text generation. GatorTronGPT improves biomedical natural language processing. We apply GatorTronGPT to generate 20 billion words of synthetic text. Synthetic NLP models trained using synthetic text generated by GatorTronGPT outperform models trained using real-world clinical text. Physicians' Turing test using 1 (worst) to 9 (best) scale shows that there are no significant differences in linguistic readability (p = 0.22; 6.57 of GatorTronGPT compared with 6.93 of human) and clinical relevance (p = 0.91; 7.0 of GatorTronGPT compared with 6.97 of human) and that physicians cannot differentiate them (p < 0.001). This study provides insights into the opportunities and challenges of LLMs for medical research and healthcare.

Real-world Effectiveness of BNT162b2 Against Infection and Severe Diseases in Children and Adolescents

Background

The efficacy of the BNT162b2 vaccine in pediatrics was assessed by randomized trials before the Omicron variant's emergence. The long-term durability of vaccine protection in this population during the Omicron period remains limited.

Objective

To assess the effectiveness of BNT162b2 in preventing infection and severe diseases with various strains of the SARS-CoV-2 virus in previously uninfected children and adolescents.

Design

Comparative effectiveness research accounting for underreported vaccination in three study cohorts: adolescents (12 to 20 years) during the Delta phase, children (5 to 11 years) and adolescents (12 to 20 years) during the Omicron phase.

Setting

A national collaboration of pediatric health systems (PEDSnet).

Participants

77,392 adolescents (45,007 vaccinated) in the Delta phase, 111,539 children (50,398 vaccinated) and 56,080 adolescents (21,180 vaccinated) in the Omicron period.

Exposures

First dose of the BNT162b2 vaccine vs. no receipt of COVID-19 vaccine.

Measurements

Outcomes of interest include documented infection, COVID-19 illness severity, admission to an intensive care unit (ICU), and cardiac complications. The effectiveness was reported as (1-relative risk)*100% with confounders balanced via propensity score stratification.

Results

During the Delta period, the estimated effectiveness of BNT162b2 vaccine was 98.4% (95% CI, 98.1 to 98.7) against documented infection among adolescents, with no significant waning after receipt of the first dose. An analysis of cardiac complications did not find an increased risk after vaccination. During the Omicron period, the effectiveness against documented infection among children was estimated to be 74.3% (95% CI, 72.2 to 76.2). Higher levels of effectiveness were observed against moderate or severe COVID-19 (75.5%, 95% CI, 69.0 to 81.0) and ICU admission with COVID-19 (84.9%, 95% CI, 64.8 to 93.5). Among adolescents, the effectiveness against documented Omicron infection was 85.5% (95% CI, 83.8 to 87.1), with 84.8% (95% CI, 77.3 to 89.9) against moderate or severe COVID-19, and 91.5% (95% CI, 69.5 to 97.6)) against ICU admission with COVID-19. The effectiveness of the BNT162b2 vaccine against the Omicron variant declined after 4 months following the first dose and then stabilized. The analysis revealed a lower risk of cardiac complications in the vaccinated group during the Omicron variant period.

Limitations

Observational study design and potentially undocumented infection.

Conclusions

Our study suggests that BNT162b2 was effective for various COVID-19-related outcomes in children and adolescents during the Delta and Omicron periods, and there is some evidence of waning effectiveness over time.

Primary Funding Source

National Institutes of Health.