Assessment of Blood Pressure and Heart Rate Related Variables in Acute Stroke Patients Receiving Intravenous Antihypertensive Medication Infusions.

BACKGROUND: We performed an analysis of a large intensive care unit electronic database to provide preliminary estimates of various blood pressure parameters in patients with acute stroke receiving intravenous (IV) antihypertensive medication and determine the relationship with in-hospital outcomes. METHODS: We identified the relationship between pre-treatment and post-treatment systolic blood pressure (SBP) and heart rate (HR)-related variables and in-hospital mortality and acute kidney injury in patients with acute stroke receiving IV clevidipine, nicardipine, or nitroprusside using data provided in the Medical Information Mart for Intensive Care (MIMIC) IV database. RESULTS: A total of 1830 patients were treated with IV clevidipine (n = 64), nicardipine (n = 1623), or nitroprusside (n = 143). The standard deviations [SDs] of pre-treatment SBP (16.3 vs. 13.7, p ≤ 0.001) and post-treatment SBP (15.4 vs. 14.4, p = 0.004) were higher in patients who died compared with those who survived, particularly in patients with intracerebral hemorrhage (ICH). The mean SBP was significantly lower post treatment compared with pre-treatment values for clevidipine (130.7 mm Hg vs. 142.5 mm Hg, p = 0.006), nicardipine (132.8 mm Hg vs. 141.6 mm Hg, p ≤ 0.001), and nitroprusside (126.2 mm Hg vs. 139.6 mm Hg, p ≤ 0.001). There were no differences in mean SDs post treatment compared with pre-treatment values for clevidipine (14.5 vs. 13.5, p = 0.407), nicardipine (14.2 vs. 14.6, p = 0.142), and nitroprusside (14.8 vs. 14.8, p = 0.997). The SDs of pre-treatment and post-treatment SBP were not significantly different in patients with ischemic stroke treated with IV clevidipine, nicardipine, or nitroprusside or for patients with ICH treated with IV clevidipine or nitroprusside. However, patients with ICH treated with IV nicardipine had a significantly higher SD of post-treatment SBP (13.1 vs. 14.2, p = 0.0032). CONCLUSIONS: We found that SBP fluctuations were associated with in-hospital mortality in patients with acute stroke. IV antihypertensive medication reduced SBP but did not reduce SBP fluctuations in this observational study. Our results highlight the need for optimizing therapeutic interventions to reduce SBP fluctuations in patients with acute stroke.

Identifying gene expression programs in single-cell RNA-seq data using linear correlation explanation.

OBJECTIVE: Gene expression analysis through single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of gene regulation in diverse cell types, tissues, and organisms. While existing methods primarily focus on identifying cell type-specific gene expression programs (GEPs), the characterization of GEPs associated with biological processes and stimuli responses remains limited. In this study, we aim to infer biologically meaningful GEPs that are associated with both cellular phenotypes and activity programs directly from scRNA-seq data. METHODS: We applied linear CorEx, a machine-learning-based approach, to infer GEPs by grouping genes based on total correlation optimization function in simulated and real-world scRNA-seq datasets. Additionally, we utilized a transfer learning approach to project CorEx-inferred GEPs to other scRNA-seq datasets. RESULTS: By leveraging total correlation optimization, linear CorEx groups genes and demonstrates superior performance in identifying cell types and activity programs compared to similar methods using simulated data. Furthermore, we apply this same approach to real-world scRNA-seq data from the mouse dentate gyrus and embryonic colon development, uncovering biologically relevant GEPs related to cell types, developmental ages, and cell cycle programs. We also demonstrate the potential for transfer learning by evaluating similar datasets, showcasing the cross-species sensitivity of linear CorEx. CONCLUSION: Our findings validate linear CorEx as a valuable tool for comprehensively analyzing complex signals in scRNA-seq data, leading to deeper insights into gene expression dynamics, cellular heterogeneity, and regulatory mechanisms.

Identification of Genomic Signatures for Colorectal Cancer Survival Using Exploratory Data Mining.

Clinicopathological presentations are critical for establishing a postoperative treatment regimen in Colorectal Cancer (CRC), although the prognostic value is low in Stage 2 CRC. We implemented a novel exploratory algorithm based on artificial intelligence (explainable artificial intelligence, XAI) that integrates mutational and clinical features to identify genomic signatures by repurposing the FoundationOne Companion Diagnostic (F1CDx) assay. The training data set (n = 378) consisted of subjects with recurrent and non-recurrent Stage 2 or 3 CRC retrieved from TCGA. Genomic signatures were built for identifying subgroups in Stage 2 and 3 CRC patients according to recurrence using genomic parameters and further associations with the clinical presentation. The summarization of the top-performing genomic signatures resulted in a 32-gene genomic signature that could predict tumor recurrence in CRC Stage 2 patients with high precision. The genomic signature was further validated using an independent dataset (n = 149), resulting in high-precision prognosis (AUC: 0.952; PPV = 0.974; NPV = 0.923). We anticipate that our genomic signatures and NCCN guidelines will improve recurrence predictions in CRC molecular stratification.

Diabetes status and other factors as correlates of risk for thrombotic and thromboembolic events during SARS-CoV-2 infection: A nationwide retrospective case-control study using Cerner Real-World Data™.

OBJECTIVES: We sought to examine in individuals with SARS-CoV-2 infection whether risk for thrombotic and thromboembolic events (TTE) is modified by presence of a diabetes diagnosis. Furthermore, we analysed whether differential risk for TTEs exists in type 1 diabetes mellitus (T1DM) versus type 2 diabetes mellitus (T2DM). DESIGN: Retrospective case-control study. SETTING: The December 2020 version of the Cerner Real-World Data COVID-19 database is a deidentified, nationwide database containing electronic medical record (EMR) data from 87 US-based health systems. PARTICIPANTS: We analysed EMR data for 322 482 patients >17 years old with suspected or confirmed SARS-CoV-2 infection who received care between December 2019 and mid-September 2020. Of these, 2750 had T1DM; 57 811 had T2DM; and 261 921 did not have diabetes. OUTCOME: TTE, defined as presence of a diagnosis code for myocardial infarction, thrombotic stroke, pulmonary embolism, deep vein thrombosis or other TTE. RESULTS: Odds of TTE were substantially higher in patients with T1DM (adjusted OR (AOR) 2.23 (1.93-2.59)) and T2DM (AOR 1.52 (1.46-1.58)) versus no diabetes. Among patients with diabetes, odds of TTE were lower in T2DM versus T1DM (AOR 0.84 (0.72-0.98)). CONCLUSIONS: Risk of TTE during COVID-19 illness is substantially higher in patients with diabetes. Further, risk for TTEs is higher in those with T1DM versus T2DM. Confirmation of increased diabetes-associated clotting risk in future studies may warrant incorporation of diabetes status into SARS-CoV-2 infection treatment algorithms.

Self-sovereign identity empowered non-fungible patient tokenization for health information exchange using blockchain technology.

BACKGROUND: Patient tokenization is a novel approach that allows anonymous patient-level linkage across healthcare facilities, minimizing the risk of breaching protected health information in health information exchange (HIE). Most patient tokenization is the centralized approach that is unable to address data security concerns fundamentally. Non-Fungible Tokens (NFT), which are non-transferable cryptographic assets on the blockchain, have the potential to provide secure, decentralized, and trustworthy patient tokenization. Self-Sovereign Identity (SSI) is a user-centric approach to verify the ownership of NFTs in a decentralized manner. METHODS: We have developed a blockchain architecture that contains four modules: (1) Creation module for NFTs creation, (2) Linkage module to link the local patients' accounts to their NFTs, (3) Authentication module that allows patients to permit healthcare providers to access their token, and (4) Exchange module, which involves the HIE process and the validation of the legitimacy of the token through SSI. RESULTS: A case study has been conducted on the proposed architecture. Over 3 million transactions have been completed successfully with a blockchain validation and written time of 1.17 s on average. A stability test has also been conducted with a higher throughput of 200 transactions per second running for an hour with an average transaction processing time of 1.42 s. CONCLUSIONS: This study proposed a blockchain architecture that achieves SSI-enabled NFT-based patient tokenization. Our architecture design, implementation, and case studies have demonstrated the feasibility and potential of NFT with SSI to establish a secure, transparent, and patient-centric identity management and HIE.

Understanding common key indicators of successful and unsuccessful cancer drug trials using a contrast mining framework on ClinicalTrials.gov.

Clinical trials are essential to the process of new drug development. As clinical trials involve significant investments of time and money, it is crucial for trial designers to carefully investigate trial settings prior to designing a trial. Utilizing trial documents from ClinicalTrials.gov, we aim to understand the common characteristics of successful and unsuccessful cancer drug trials to provide insights about what to learn and what to avoid. In this research, we first computationally classified cancer drug trials into successful and unsuccessful cases and then utilized natural language processing to extract eligibility criteria information from the trial documents. To provide explainable and potentially modifiable recommendations for new trial design, contrast mining was applied to discoverhighly contrasted patterns with a significant difference in prevalence between successful (completion with advancement to the next phase) and unsuccessful (suspended, withdrawn, or terminated) groups. Our method identified contrast patterns consisting of combinations of drug categories, eligibility criteria, study organization, and study design for nine major cancers. In addition to a literature review for the qualitative validation of mined contrast patterns, we found that contrast-pattern-based classifiers using the top 200 contrast patterns as feature representations can achieve approximately 80% F1 score for eight out of ten cancer types in our experiments. In summary, aligning with the modernization efforts of ClinicalTrials.gov, our study demonstrates that understanding the contrast characteristics of successful and unsuccessful cancer trials may provide insights into the decision-making process for trial investigators and therefore facilitate improved cancer drug trial design.

COVID-Specific Long-term Sequelae in Comparison to Common Viral Respiratory Infections: An Analysis of 17 487 Infected Adult Patients.

Background: A better understanding of long-term health effects after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has become one of the health care priorities in the current pandemic. We analyzed a large and diverse patient cohort to study health effects related to SARS-CoV-2 infection occurring >1 month postinfection. Methods: We analyzed 17 487 patients who received diagnoses for SARS-CoV-2 infection in a total of 122 health care facilities in the United States before April 14, 2022. Patients were propensity score-matched with patients diagnosed with the common cold, influenza, or viral pneumonia from March 1, 2020, to April 1, 2021. For each outcome, SARS-CoV-2 was compared with a generic viral respiratory infection (VRI) by predicting diagnoses in the period between 30 and 365 days postinfection. Both coronavirus disease 2019 (COVID-19) and VRI patients were propensity score-matched with patients with no record of COVID-19 or VRI, and the same methodology was applied. Diagnoses where COVID-19 infection was a significant positive predictor in both COVID-19 vs VRI and COVID-19 vs control comparisons were considered COVID-19-specific effects. Results: Compared with common VRIs, SARS-CoV-2 was associated with diagnoses of palpitations, hair loss, fatigue, chest pain, dyspnea, joint pain, and obesity in the postinfectious period. Conclusions: We identify that some diagnoses commonly described as "long COVID" do not appear significantly more frequent post-COVID-19 infection compared with other common VRIs. We also identify sequelae that are specifically associated with a prior SARS-CoV-2 infection.

Liver injury on admission linked to worse outcomes in COVID-19: an analysis of 14,138 patients.

Background: Infection with the SARS-CoV-2 virus, which can result in hepatic inflammation and injury that varies from mild to severe and potentially acute fulminant liver injury, may be associated with poor outcomes. Our aims were to: (I) assess baseline clinical and demographic characteristics in patients with coronavirus disease 2019 (COVID-19) who did and did not have abnormalities in liver chemistries [alkaline phosphatase (ALP), aspartate aminotransferase (AST), alanine aminotransferase (ALT), and total bilirubin (Tbili)] and (II) evaluate associations between abnormalities in liver chemistries and the primary outcomes of in-hospital death, intubation, and hospital length of stay (LOS). Methods: In this nationwide retrospective cohort study of 14,138 patients, we analyzed associations between abnormalities in liver chemistries (ALT, AST, ALP, and Tbili) and mortality, intubation, and prolonged hospital LOS in patients with laboratory-confirmed COVID-19. We used Pearson's chi-squared tests to detect significant differences in categorical variables for patients with and without abnormal liver chemistries. Welch's two-sample t-tests were used to make comparisons of liver chemistry (ALT, AST, ALP, Tbili) and serum albumin results. All other continuous variables were analyzed using independent samples t-tests. A P value of <0.05 was considered significant. Results: Propensity score matching demonstrated that abnormalities in liver chemistries at admission are significantly associated with increased risk for mortality (RR 1.70) and intubation (RR 1.44) in patients with COVID-19. Elevated AST is the liver chemistry abnormality associated with the highest risk for mortality (RR 2.27), intubation (RR 2.12), and prolonged hospitalization (RR 1.19). Male gender, pre-existing liver disease, and decreased serum albumin are also significantly associated with severe outcomes and death in COVID-19. Conclusions: Routine liver chemistry testing should be implemented and used for risk stratification at the time of COVID-19 diagnosis.

Diagnostic and treatment concordance in primary care participants and dermatologists utilizing Extension for Community Health Outcomes (ECHO).

INTRODUCTION: Suboptimal access to dermatologic care is dependent on patient location and insurance type. Although there have been attempts to address access issues, barriers to providing excellent dermatologic care to all patients at the right time still exist. The objective of this study was to investigate the clinical impact of Dermatology Extension for Community Healthcare Outcomes (ECHO) project participation on primary care providers' diagnostic and treatment tendencies and accuracy. METHODS: This was a retrospective cohort study constructed using Dermatology Extension for Community Healthcare Outcomes case and recommendation data from November 2015 to June 2021. The University of Missouri-based Dermatology Extension for Community Healthcare Outcomes specialty hub team offers regularly scheduled live interactive tele-mentoring sessions for primary care providers who practice in rural and underserved areas. 524 patient cases presented by 25 primary care providers were included in the analysis. Of those, 449 cases were included in diagnostic concordance, and 451 in treatment concordance analysis. RESULTS: Less than 40% of all diagnoses were fully concordant with an expert panel. Over 33% of patients were misdiagnosed, and over 26% received partially correct diagnosis. Only 16% of all treatment recommendations were fully concordant with an expert panel. DISCUSSION: Diagnostic and treatment accuracy of participants is low, and Dermatology Extension for Community Healthcare Outcomes platform ensured patients received correct diagnosis and treatment quickly. Although tele-dermatology models are effective, they continue to be underutilized. Dermatologists in practice and training should be encouraged to adopt innovative clinical educational models, like Dermatology ECHO, to expand access to dermatologic expertise for the most marginalized populations.

New cardiovascular events in the convalescent period among survivors of SARS-CoV-2 infection.

BACKGROUND: Patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection may have an increased risk of acute cardiovascular events in the convalescent period. AIMS: To determine whether patients with SARS-CoV-2 infection have an increased risk of cardiovascular events during the convalescent period. METHODS: We analyzed 10,691 hospitalized adult pneumonia patients with SARS-CoV-2 infection and contemporary matched controls of pneumonia patients without SARS-CoV-2 infection. The risk of new cardiovascular events following >30 days pneumonia admission (convalescent period) was ascertained using Cox proportional hazards regression analysis to adjust for potential confounders. RESULTS: Among 10,691 pneumonia patients with SARS-CoV-2 infection, 697 patients (5.8%; 95% CI, 5.4-6.2%) developed new cardiovascular events (median time interval of 218 days post pneumonia admission; interquartile range Q1 = 117 days, Q3 = 313 days). The risk of new cardiovascular events was not significantly higher among pneumonia patients with SARS-CoV-2 infection compared with those with pneumonia without SARS-CoV-2 infection (hazard ratio (HR), 0.90, 95% CI, 0.80-1.02) after adjustment for potential confounders. In addition, no significant difference in the rate of a new ischemic stroke (HR, 0.84; 95% CI, 0.70-1.02) or ischemic heart disease (HR, 1.00; 95% CI, 0.87-1.15) was observed between the pneumonia patients with and without SARS-CoV-2 infection. CONCLUSION: Our study suggests that new cardiovascular events rate in the convalescent period among pneumonia patients with SARS-CoV-2 infection was not significantly higher than the rate seen with other pneumonias.

Outcomes with IV tenecteplase and IV alteplase for acute ischemic stroke with or without thrombectomy in real-world settings in the United States.

BACKGROUND AND PURPOSE: Although many stroke centers in United States are using intravenous (IV) tenecteplase (TNK) for acute ischemic stroke patients, there is paucity of comparative data between IV TNK and IV alteplase from real-world settings. MATERIALS AND METHODS: We analyzed the data from 122 healthcare facilities in Cerner Real World Data and included patients admitted between February 2016 to April 2022 to determine the effect of IV TNK (compared with IV alteplase) on occurrence of two outcomes in acute ischemic stroke patients stratified by use of thrombectomy: non-routine discharge or death, and intracranial hemorrhage after adjusting for potential confounders. RESULTS: Among 30,643 acute ischemic stroke patients analyzed, 29,480 (96.2%) and 1,163 (3.8%) patients received IV alteplase and IV TNK, respectively. The proportion of patients who received thrombectomy was significantly higher among patients who received IV TNK compared with those who received IV alteplase (16.7% versus 11.0%, p<0.001). Occurrence of intracranial hemorrhage was more common among patients treated with IV TNK in acute ischemic stroke patients who did not receive thrombectomy (7.9% versus 5.1%, p<0.001) but not in those who received thrombectomy (20.1% versus 16.8%, p = 0.234). In the logistic regression analysis, patients treated with IV TNK who did not receive thrombectomy were at higher risk of intracranial hemorrhage (OR, 1.34, 95% CI 1.05-1.72, p = 0.02) after adjusting for age (age strata), gender, race/ethnicity, hypertension, diabetes mellitus, atrial fibrillation, hyperlipidemia, malignancy, nicotine dependence, previous ischemic stroke, previous transient ischemic attack, previous intracerebral hemorrhage, previous subarachnoid hemorrhage, previous acute myocardial infarction, atherosclerosis of aorta, previous AKI, congestive heart failure, peripheral vascular disease, and hospital type, aphasia, hemiplegia, neglect, somnolence, stupor and coma, dysphagia, and homonymous hemianopsia. There was no difference in the rate of non-routine discharge or death between patients treated with IV TNK and those treated with IV alteplase in the multivariate analyses. CONCLUSIONS: In an analysis of real-world data, IV TNK was associated with higher rates of intracranial hemorrhage compared with IV alteplase in patients with acute ischemic stroke who did not undergo thrombectomy.

Immune-Related Gene Signatures to Predict the Effectiveness of Chemoimmunotherapy in Triple-Negative Breast Cancer Using Exploratory Subgroup Discovery.

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer with limited therapeutic options. Although immunotherapy has shown potential in TNBC patients, clinical studies have only demonstrated a modest response. Therefore, the exploration of immunotherapy in combination with chemotherapy is warranted. In this project we identified immune-related gene signatures for TNBC patients that may explain differences in patients' outcomes after anti-PD-L1+chemotherapy treatment. First, we ran the exploratory subgroup discovery algorithm on the TNBC dataset comprised of 422 patients across 24 studies. Secondly, we narrowed down the search to twelve homogenous subgroups based on tumor mutational burden (TMB, low or high), relapse status (disease-free or recurred), tumor cellularity (high, low and moderate), menopausal status (pre- or post) and tumor stage (I, II and III). For each subgroup we identified a union of the top 10% of genotypic patterns. Furthermore, we employed a multinomial regression model to predict significant genotypic patterns that would be linked to partial remission after anti-PD-L1+chemotherapy treatment. Finally, we uncovered distinct immune cell populations (T-cells, B-cells, Myeloid, NK-cells) for TNBC patients with various treatment outcomes. CD4-Tn-LEF1 and CD4-CXCL13 T-cells were linked to partial remission on anti-PD-L1+chemotherapy treatment. Our informatics pipeline may help to select better responders to chemoimmunotherapy, as well as pinpoint the underlying mechanisms of drug resistance in TNBC patients at single-cell resolution.

Impact of diabetes status and related factors on COVID-19-associated hospitalization: A nationwide retrospective cohort study of 116,370 adults with SARS-CoV-2 infection.

AIMS: We examined diabetes status (no diabetes; type 1 diabetes [T1D]; type 2 diabetes [T2D]) and other demographic and clinical factors as correlates of coronavirus disease 2019 (COVID-19)-related hospitalization. Further, we evaluated predictors of COVID-19-related hospitalization in T1D and T2D. METHODS: We analyzed electronic health record data from the de-identified COVID-19 database (December 2019 through mid-September 2020; 87 US health systems). Logistic mixed models were used to examine predictors of hospitalization at index encounters associated with confirmed SARS-CoV-2 infection. RESULTS: In 116,370 adults (>=18 years old) with COVID-19 (93,098 no diabetes; 802 T1D; 22,470 T2D), factors that independently increased risk for hospitalization included diabetes, male sex, public health insurance, decreased body mass index (BMI; <25.0-29.9 kg/m2), increased BMI (>25.0-29.9 kg/m2), vitamin D deficiency/insufficiency, and Elixhauser comorbidity score. After further adjustment for concurrent hyperglycemia and acidosis in those with diabetes, hospitalization risk was substantially higher in T1D than T2D and in those with low vitamin D and elevated hemoglobin A1c (HbA1c). CONCLUSIONS: The higher hospitalization risk in T1D versus T2D warrants further investigation. Modifiable risk factors such as vitamin D deficiency/insufficiency, BMI, and elevated HbA1c may serve as prognostic indicators for COVID-19-related hospitalization in adults with diabetes.

TBX21 Methylation as a Potential Regulator of Immune Suppression in CMS1 Subtype Colorectal Cancer.

Cytotoxic T lymphocyte (CTL) infiltration is associated with survival, recurrence, and therapeutic response in colorectal cancer (CRC). Immune checkpoint inhibitor (ICI) therapy, which requires CTLs for response, does not work for most CRC patients. Therefore, it is critical to improve our understanding of immune resistance in this disease. We utilized 2391 CRC patients and 7 omics datasets, integrating clinical and genomic data to determine how DNA methylation may impact survival and CTL function in CRC. Using comprehensive molecular subtype (CMS) 1 patients as reference, we found TBX21 to be the only gene with altered expression and methylation that was associated with CTL infiltration. We found that CMS1 patients with high TBX21 expression and low methylation had a significant survival advantage. To confirm the role of Tbx21 in CTL function, we utilized scRNAseq data, demonstrating the association of TBX21 with markers of enhanced CTL function. Further analysis using pathway enrichment found that the genes TBX21, MX1, and SP140 had altered expression and methylation, suggesting that the TP53/P53 pathway may modify TBX21 methylation to upregulate TBX21 expression. Together, this suggests that targeting epigenetic modification more specifically for therapy and patient stratification may provide improved outcomes in CRC.

Identification of Immuno-Targeted Combination Therapies Using Explanatory Subgroup Discovery for Cancer Patients with EGFR Wild-Type Gene.

(1) Background: Phenotypic and genotypic heterogeneity are characteristic features of cancer patients. To tackle patients' heterogeneity, immune checkpoint inhibitors (ICIs) represent some the most promising therapeutic approaches. However, approximately 50% of cancer patients that are eligible for treatment with ICIs do not respond well, especially patients with no targetable mutations. Over the years, multiple patient stratification techniques have been developed to identify homogenous patient subgroups, although matching a patient subgroup to a treatment option that can improve patients' health outcomes remains a challenging task. (2) Methods: We extended our Subgroup Discovery algorithm to identify patient subpopulations that could potentially benefit from immuno-targeted combination therapies in four cancer types: head and neck squamous carcinoma (HNSC), lung adenocarcinoma (LUAD), lung squamous carcinoma (LUSC), and skin cutaneous melanoma (SKCM). We employed the proportional odds model to identify significant drug targets and the corresponding compounds that increased the likelihood of stable disease versus progressive disease in cancer patients with the EGFR wild-type (WT) gene. (3) Results: Our pipeline identified six significant drug targets and thirteen specific compounds for cancer patients with the EGFR WT gene. Three out of six drug targets-FCGR2B, IGF1R, and KIT-substantially increased the odds of having stable disease versus progressive disease. Progression-free survival (PFS) of more than 6 months was a common feature among the investigated subgroups. (4) Conclusions: Our approach could help to better select responders for immuno-targeted combination therapies and improve health outcomes for cancer patients with no targetable mutations.

Somatic mutation variant analysis in rural, resectable non-small cell lung carcinoma patients.

Rural non-small cell lung cancer (NSCLC) patients do worse, largely related to lack of access to care. In this study, the mutational characteristics and potential for targeted therapy in rural, resectable NSCLC patients using whole exome sequencing (WES) were analyzed. WES was performed on tumor-adjacent normal pairs from rural patients undergoing resection for NSCLC. Sequencing alignment, variant-calling, annotation, and tumor mutational burden (TMB) calculations were performed using standard methods. cBioportal and OncoKB were used for comparisons of mutational frequencies and actionable targets. Thirty-four NSCLC patients underwent WES after surgical resection. The gene most frequently containing somatic variants was TP53. The median number of somatic variants was 188 (Range 11-1056), and median TMB was 3.30 (0.33-18.56) nonsynonymous mutations per Mb. Tumor stage and survival were not associated with number of variants, TMB or TP53 mutational status. Significant concordance among the most common mutations when cross-referenced to cBioportal (R = 0.78, p < 0.0001) was observed. 24% of patients had variants in actionable genes based on OncoKB annotation. In summary, we demonstrate baseline mutational frequency and establish foundations for targeted adjuvant trials in rural NSCLC patients with specific differences. Future studies must ensure to include rural patients to improve NSCLC patient outcomes.

Measurement of lipid flux to advance translational research: evolution of classic methods to the future of precision health.

Over the past 70 years, the study of lipid metabolism has led to important discoveries in identifying the underlying mechanisms of chronic diseases. Advances in the use of stable isotopes and mass spectrometry in humans have expanded our knowledge of target molecules that contribute to pathologies and lipid metabolic pathways. These advances have been leveraged within two research paths, leading to the ability (1) to quantitate lipid flux to understand the fundamentals of human physiology and pathology and (2) to perform untargeted analyses of human blood and tissues derived from a single timepoint to identify lipidomic patterns that predict disease. This review describes the physiological and analytical parameters that influence these measurements and how these issues will propel the coming together of the two fields of metabolic tracing and lipidomics. The potential of data science to advance these fields is also discussed. Future developments are needed to increase the precision of lipid measurements in human samples, leading to discoveries in how individuals vary in their production, storage, and use of lipids. New techniques are critical to support clinical strategies to prevent disease and to identify mechanisms by which treatments confer health benefits with the overall goal of reducing the burden of human disease.

Community Case Study: The Role of Live-Interactive Learning Network in Addressing Community Wide Information Needs Through Show-Me COVID-19 ECHO.

After the COVID-19 pandemic reached Missouri, the Show-Me ECHO (Extension for Community Healthcare Outcomes) project initiated COVID-19 ECHO virtual knowledge-sharing networking sessions. These live-interactive weekly sessions inform participants about up-to-date evidence-based recommendations and guidelines through expert didactic lectures followed by real-life case discussions. We conducted a qualitative analysis of pre-session surveys and questions asked during sessions to learn about information needs of community members during first months of public health emergency. This was a pilot project using qualitative analysis of registration questions regarding anticipated COVID-19 community information needs, and participants' questions asked during sessions collected from March 23 until May 4, 2020. We also analyzed participants' satisfaction surveys collected in December 2020. A total of 761 unique participants attended COVID-19 ECHO during the study period. Survey was completed by 692 respondents. Participants asked 315 questions resulting in 797 identified community information needs. Five thematic categories were recognized: patient care, information seeking, minimizing exposure, financial themes, and general comments. Most attendees rated content quality, logistics, and technical operations as good or excellent on a five-point Likert scale. The COVID-19 ECHO model was responsive to the needs of participants by sharing and discussing up-to-date recommendations and guidelines regarding COVID-19. Sessions were well-attended, and the didactic presenters were invited to deliver same or similar presentations at Boone County Medical Society (BCMS) weekly seminars, suggesting the value of the project to healthcare providers and other community members caring for or working with the most vulnerable populations.

Re-engineering a Clinical Trial Management System Using Blockchain Technology: System Design, Development, and Case Studies.

BACKGROUND: A clinical trial management system (CTMS) is a suite of specialized productivity tools that manage clinical trial processes from study planning to closeout. Using CTMSs has shown remarkable benefits in delivering efficient, auditable, and visualizable clinical trials. However, the current CTMS market is fragmented, and most CTMSs fail to meet expectations because of their inability to support key functions, such as inconsistencies in data captured across multiple sites. Blockchain technology, an emerging distributed ledger technology, is considered to potentially provide a holistic solution to current CTMS challenges by using its unique features, such as transparency, traceability, immutability, and security. OBJECTIVE: This study aimed to re-engineer the traditional CTMS by leveraging the unique properties of blockchain technology to create a secure, auditable, efficient, and generalizable CTMS. METHODS: A comprehensive, blockchain-based CTMS that spans all stages of clinical trials, including a sharable trial master file system; a fast recruitment and simplified enrollment system; a timely, secure, and consistent electronic data capture system; a reproducible data analytics system; and an efficient, traceable payment and reimbursement system, was designed and implemented using the Quorum blockchain. Compared with traditional blockchain technologies, such as Ethereum, Quorum blockchain offers higher transaction throughput and lowers transaction latency. Case studies on each application of the CTMS were conducted to assess the feasibility, scalability, stability, and efficiency of the proposed blockchain-based CTMS. RESULTS: A total of 21.6 million electronic data capture transactions were generated and successfully processed through blockchain, with an average of 335.4 transactions per second. Of the 6000 patients, 1145 were matched in 1.39 seconds using 10 recruitment criteria with an automated matching mechanism implemented by the smart contract. Key features, such as immutability, traceability, and stability, were also tested and empirically proven through case studies. CONCLUSIONS: This study proposed a comprehensive blockchain-based CTMS that covers all stages of the clinical trial process. Compared with our previous research, the proposed system showed an overall better performance. Our system design, implementation, and case studies demonstrated the potential of blockchain technology as a potential solution to CTMS challenges and its ability to perform more health care tasks.

New-Onset Dementia Among Survivors of Pneumonia Associated With Severe Acute Respiratory Syndrome Coronavirus 2 Infection.

Background: Case series without control groups suggest that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection may result in cognitive deficits and dementia in the postinfectious period. Methods: Adult pneumonia patients with SARS-CoV-2 infection (index hospitalization) and age-, gender-, and race/ethnicity-matched contemporary control pneumonia patients without SARS-CoV-2 infection were identified from 110 healthcare facilities in United States. The risk of new diagnosis of dementia following >30 days after the index hospitalization event without any previous history of dementia was identified using logistic regression analysis to adjust for potential confounders. Results: Among 10 403 patients with pneumonia associated with SARS-CoV-2 infection, 312 patients (3% [95% confidence interval {CI}, 2.7%-3.4%]) developed new-onset dementia over a median period of 182 days (quartile 1 = 113 days, quartile 3 = 277 days). After adjustment for age, gender, race/ethnicity, hypertension, diabetes mellitus, hyperlipidemia, nicotine dependence/tobacco use, alcohol use/abuse, atrial fibrillation, previous stroke, and congestive heart failure, the risk of new-onset dementia was significantly higher with pneumonia associated with SARS-CoV-2 infection compared with pneumonia unrelated to SARS-CoV-2 infection (odds ratio [OR], 1.3 [95% CI, 1.1-1.5]). The association remained significant after further adjustment for occurrence of stroke, septic shock, and intubation/mechanical ventilation during index hospitalization (OR, 1.3 [95% CI, 1.1-1.5]). Conclusions: Approximately 3% of patients with pneumonia associated with SARS-CoV-2 infection developed new-onset dementia, which was significantly higher than the rate seen with other pneumonias.