Identification of risk factors of Long COVID and predictive modeling in the RECOVER EHR cohorts.

BACKGROUND: SARS-CoV-2-infected patients may develop new conditions in the period after the acute infection. These conditions, the post-acute sequelae of SARS-CoV-2 infection (PASC, or Long COVID), involve a diverse set of organ systems. Limited studies have investigated the predictability of Long COVID development and its associated risk factors. METHODS: In this retrospective cohort study, we used electronic healthcare records from two large-scale PCORnet clinical research networks, INSIGHT (~1.4 million patients from New York) and OneFlorida+ (~0.7 million patients from Florida), to identify factors associated with having Long COVID, and to develop machine learning-based models for predicting Long COVID development. Both SARS-CoV-2-infected and non-infected adults were analysed during the period of March 2020 to November 2021. Factors associated with Long COVID risk were identified by removing background associations and correcting for multiple tests. RESULTS: We observed complex association patterns between baseline factors and a variety of Long COVID conditions, and we highlight that severe acute SARS-CoV-2 infection, being underweight, and having baseline comorbidities (e.g., cancer and cirrhosis) are likely associated with increased risk of developing Long COVID. Several Long COVID conditions, e.g., dementia, malnutrition, chronic obstructive pulmonary disease, heart failure, PASC diagnosis U099, and acute kidney failure are well predicted (C-index > 0.8). Moderately predictable conditions include atelectasis, pulmonary embolism, diabetes, pulmonary fibrosis, and thromboembolic disease (C-index 0.7-0.8). Less predictable conditions include fatigue, anxiety, sleep disorders, and depression (C-index around 0.6). CONCLUSIONS: This observational study suggests that association patterns between investigated factors and Long COVID are complex, and the predictability of different Long COVID conditions varies. However, machine learning-based predictive models can help in identifying patients who are at risk of developing a variety of Long COVID conditions.

Most people who develop COVID-19 make a full recovery, but some go on to develop post-acute sequelae of SARS-CoV-2 infection, commonly known as Long COVID. Up to now, we did not know why some people are affected by Long COVID whilst others are not. We conducted a study to identify risk factors for Long COVID and developed a mathematical modeling approach to predict those at risk. We find that Long COVID is associated with some factors such as experiencing severe acute COVID-19, being underweight, and having conditions including cancer or cirrhosis. Due to the wide variety of symptoms defined as Long COVID, it may be challenging to come up with a set of risk factors that can predict the whole spectrum of Long COVID. However, our approach could be used to predict a variety of Long COVID conditions.

Association of computed tomography scan-assessed body composition with immune and PI3K/AKT pathway proteins in distinct breast cancer tumor components.

This hypothesis-generating study aims to examine the extent to which computed tomography-assessed body composition phenotypes are associated with immune and PI3K/AKT signaling pathways in breast tumors. A total of 52 patients with newly diagnosed breast cancer were classified into four body composition types: adequate (lowest two tertiles of total adipose tissue [TAT]) and highest two tertiles of total skeletal muscle [TSM] areas); high adiposity (highest tertile of TAT and highest two tertiles of TSM); low muscle (lowest tertile of TSM and lowest two tertiles of TAT); and high adiposity with low muscle (highest tertile of TAT and lowest tertile of TSM). Immune and PI3K/AKT pathway proteins were profiled in tumor epithelium and the leukocyte-enriched stromal microenvironment using GeoMx (NanoString). Linear mixed models were used to compare log2-transformed protein levels. Compared with the normal type, the low muscle type was associated with higher expression of INPP4B (log2-fold change = 1.14, p = 0.0003, false discovery rate = 0.028). Other significant associations included low muscle type with increased CTLA4 and decreased pan-AKT expression in tumor epithelium, and high adiposity with increased CD3, CD8, CD20, and CD45RO expression in stroma (P<0.05; false discovery rate >0.2). With confirmation, body composition can be associated with signaling pathways in distinct components of breast tumors, highlighting the potential utility of body composition in informing tumor biology and therapy efficacies.

Effect of Eligibility Criteria on Patients' Survival and Serious Adverse Events in Colorectal Cancer Drug Trials.

This study investigates the impact of clinical trial eligibility criteria on patient survival and serious adverse events (SAEs) in colorectal cancer (CRC) drug trials using real-world data. We utilized the OneFlorida+ network's data repository, conducting a retrospective analysis of CRC patients receiving FDA-approved first-line metastatic treatments. Propensity score matching created balanced case-control groups, which were evaluated using survival analysis and machine learning algorithms to assess the effects of eligibility criteria. Our study included 68,375 patients, with matched case-control groups comprising 1,126 patients each. Survival analysis revealed ethnicity and race, along with specific medical history (eligibility criteria), as significant survival outcome predictors. Machine learning models, particularly the XgBoost regressor, were employed to analyze SAEs, indicating that age and study groups were notable factors in SAEs occurrence. The study's findings highlight the importance of considering patient demographics and medical history in CRC trial designs.

Gra-CRC-miRTar: The pre-trained nucleotide-to-graph neural networks to identify potential miRNA targets in colorectal cancer.

Colorectal cancer (CRC) is the third most diagnosed cancer and the second deadliest cancer worldwide representing a major public health problem. In recent years, increasing evidence has shown that microRNA (miRNA) can control the expression of targeted human messenger RNA (mRNA) by reducing their abundance or translation, acting as oncogenes or tumor suppressors in various cancers, including CRC. Due to the significant up-regulation of oncogenic miRNAs in CRC, elucidating the underlying mechanism and identifying dysregulated miRNA targets may provide a basis for improving current therapeutic interventions. In this paper, we proposed Gra-CRC-miRTar, a pre-trained nucleotide-to-graph neural network framework, for identifying potential miRNA targets in CRC. Different from previous studies, we constructed two pre-trained models to encode RNA sequences and transformed them into de Bruijn graphs. We employed different graph neural networks to learn the latent representations. The embeddings generated from de Bruijn graphs were then fed into a Multilayer Perceptron (MLP) to perform the prediction tasks. Our extensive experiments show that Gra-CRC-miRTar achieves better performance than other deep learning algorithms and existing predictors. In addition, our analyses also successfully revealed 172 out of 201 functional interactions through experimentally validated miRNA-mRNA pairs in CRC. Collectively, our effort provides an accurate and efficient framework to identify potential miRNA targets in CRC, which can also be used to reveal miRNA target interactions in other malignancies, facilitating the development of novel therapeutics.

Identifying social determinants of health from clinical narratives: A study of performance, documentation ratio, and potential bias.

OBJECTIVE: To develop a natural language processing (NLP) package to extract social determinants of health (SDoH) from clinical narratives, examine the bias among race and gender groups, test the generalizability of extracting SDoH for different disease groups, and examine population-level extraction ratio. METHODS: We developed SDoH corpora using clinical notes identified at the University of Florida (UF) Health. We systematically compared 7 transformer-based large language models (LLMs) and developed an open-source package - SODA (i.e., SOcial DeterminAnts) to facilitate SDoH extraction from clinical narratives. We examined the performance and potential bias of SODA for different race and gender groups, tested the generalizability of SODA using two disease domains including cancer and opioid use, and explored strategies for improvement. We applied SODA to extract 19 categories of SDoH from the breast (n = 7,971), lung (n = 11,804), and colorectal cancer (n = 6,240) cohorts to assess patient-level extraction ratio and examine the differences among race and gender groups. RESULTS: We developed an SDoH corpus using 629 clinical notes of cancer patients with annotations of 13,193 SDoH concepts/attributes from 19 categories of SDoH, and another cross-disease validation corpus using 200 notes from opioid use patients with 4,342 SDoH concepts/attributes. We compared 7 transformer models and the GatorTron model achieved the best mean average strict/lenient F1 scores of 0.9122 and 0.9367 for SDoH concept extraction and 0.9584 and 0.9593 for linking attributes to SDoH concepts. There is a small performance gap (∼4%) between Males and Females, but a large performance gap (>16 %) among race groups. The performance dropped when we applied the cancer SDoH model to the opioid cohort; fine-tuning using a smaller opioid SDoH corpus improved the performance. The extraction ratio varied in the three cancer cohorts, in which 10 SDoH could be extracted from over 70 % of cancer patients, but 9 SDoH could be extracted from less than 70 % of cancer patients. Individuals from the White and Black groups have a higher extraction ratio than other minority race groups. CONCLUSIONS: Our SODA package achieved good performance in extracting 19 categories of SDoH from clinical narratives. The SODA package with pre-trained transformer models is available at https://github.com/uf-hobi-informatics-lab/SODA_Docker.

The Burden of Cancer and Pre-cancerous Conditions Among Transgender Individuals in a Large Healthcare Network.

The current study aimed to examine the prevalence of and risk factors for cancer and pre-cancerous conditions, comparing transgender and cisgender individuals, using 2012-2023 electronic health record data from a large healthcare system. We identified 2,745 transgender individuals using a previously validated computable phenotype and 54,900 matched cisgender individuals. We calculated the prevalence of cancer and pre-cancer related to human papillomavirus (HPV), human immunodeficiency virus (HIV), tobacco, alcohol, lung, breast, colorectum, and built multivariable logistic models to examine the association between gender identity and the presence of cancer or pre-cancer. Results indicated similar odds of developing cancer across gender identities, but transgender individuals exhibited significantly higher risks for pre-cancerous conditions, including alcohol-related, breast, and colorectal pre-cancers compared to cisgender women, and HPV-related, tobacco-related, alcohol-related, and colorectal pre-cancers compared to cisgender men. These findings underscore the need for tailored interventions and policies addressing cancer health disparities affecting the transgender population.

Oxymatrine protects articular chondrocytes from IL-1ß-induced damage through autophagy activation via AKT/mTOR signaling pathway inhibition.

BACKGROUND: Osteoarthritis (OA) is a common degenerative joint disease characterized by persistent articular cartilage degeneration and synovitis. Oxymatrine (OMT) is a quinzolazine alkaloid extracted from the traditional Chinese medicine, matrine, and possesses anti-inflammatory properties that may help regulate the pathogenesis of OA; however, its mechanism has not been elucidated. This study aimed to investigate the effects of OMT on interleukin-1ß (IL-1ß)-induced damage and the potential mechanisms of action. METHODS: Chondrocytes were isolated from Sprague-Dawley rats. Toluidine blue and Collagen II immunofluorescence staining were used to determine the purity of the chondrocytes. Thereafter, the chondrocytes were subjected to IL-1ß stimulation, both in the presence and absence of OMT, or the autophagy inhibitor 3-methyladenine (3-MA). Cell viability was assessed using the MTT assay and SYTOX Green staining. Additionally, flow cytometry was used to determine cell apoptosis rate and reactive oxygen species (ROS) levels. The protein levels of AKT, mTOR, LC3, P62, matrix metalloproteinase-13, and collagen II were quantitatively analyzed using western blotting. Immunofluorescence was used to assess LC3 expression. RESULTS: OMT alleviated IL-1ß-induced damage in chondrocytes, by increasing the survival rate, reducing the apoptosis rates of chondrocytes, and preventing the degradation of the cartilage matrix. In addition, OMT decreased the ROS levels and inhibited the AKT/mTOR signaling pathway while promoting autophagy in IL-1ß treated chondrocytes. However, the effectiveness of OMT in improving chondrocyte viability under IL-1ß treatment was limited when autophagy was inhibited by 3-MA. CONCLUSIONS: OMT decreases oxidative stress and inhibits the AKT/mTOR signaling pathway to enhance autophagy, thus inhibiting IL-1ß-induced damage. Therefore, OMT may be a novel and effective therapeutic agent for the clinical treatment of OA.

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.

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.

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.

Developing a computable phenotype for glioblastoma.

BACKGROUND: Glioblastoma is the most common malignant brain tumor, and thus it is important to be able to identify patients with this diagnosis for population studies. However, this can be challenging as diagnostic codes are nonspecific. The aim of this study was to create a computable phenotype (CP) for glioblastoma multiforme (GBM) from structured and unstructured data to identify patients with this condition in a large electronic health record (EHR). METHODS: We used the University of Florida (UF) Health Integrated Data Repository, a centralized clinical data warehouse that stores clinical and research data from various sources within the UF Health system, including the EHR system. We performed multiple iterations to refine the GBM-relevant diagnosis codes, procedure codes, medication codes, and keywords through manual chart review of patient data. We then evaluated the performances of various possible proposed CPs constructed from the relevant codes and keywords. RESULTS: We underwent six rounds of manual chart reviews to refine the CP elements. The final CP algorithm for identifying GBM patients was selected based on the best F1-score. Overall, the CP rule "if the patient had at least 1 relevant diagnosis code and at least 1 relevant keyword" demonstrated the highest F1-score using both structured and unstructured data. Thus, it was selected as the best-performing CP rule. CONCLUSIONS: We developed and validated a CP algorithm for identifying patients with GBM using both structured and unstructured EHR data from a large tertiary care center. The final algorithm achieved an F1-score of 0.817, indicating a high performance, which minimizes possible biases from misclassification errors.

Disparities Contributing to Late-Stage Diagnosis of Lung, Colorectal, Breast, and Cervical Cancers: Rural and Urban Poverty in Florida.

Despite advances in cancer screening, late-stage cancer diagnosis is still a major cause of morbidity and mortality in the United States. In this study, we aim to understand demographic and geographic factors associated with receiving a late-stage diagnosis (LSD) of lung, colorectal, breast, or cervical cancer. (1) Methods: We analyzed data of patients with a cancer diagnosis between 2016 and 2020 from the Florida Cancer Data System (FCDS), a statewide population-based registry. To investigate correlates of LSD, we estimated multi-variable logistic regression models for each cancer while controlling for age, sex, race, insurance, and census tract rurality and poverty. (2) Results: Patients from high-poverty rural areas had higher odds for LSD of lung (OR = 1.23, 95% CI (1.10, 1.37)) and breast cancer (OR = 1.31, 95% CI (1.17,1.47)) than patients from low-poverty urban areas. Patients in high-poverty urban areas saw higher odds of LSD for lung (OR = 1.05 95% CI (1.00, 1.09)), breast (OR = 1.10, 95% CI (1.06, 1.14)), and cervical cancer (OR = 1.19, 95% CI (1.03, 1.37)). (3) Conclusions: Financial barriers contributing to decreased access to care likely drive LSD for cancer in rural and urban communities of Florida.

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.

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.

Eye Rubbing in Chinese Patients With Keratoconus: A Multicenter Analysis.

PURPOSE: To investigate the eye rubbing habits of Chinese patients with keratoconus. METHODS: This study was carried out from 2018 to June 2022 at Shandong Eye Hospital, Qingdao Eye Hospital, and Henan Eye Hospital. The study compared the number of patients who rubbed their eyes between medical records and second time questionnaires, eye rubbing of patients with myopia and patients with keratoconus, and disease severity between patients with keratoconus. A questionnaire survey of ophthalmologists was conducted to determine their degree of awareness that eye rubbing is a risk factor for keratoconus. RESULTS: The study assessed 799 patients with keratoconus and 798 control patients, and 97 ophthalmologists. The average proportion of patients with keratoconus who rubbed their eyes was 31.0% in the medical records with an increasing trend related to the increase in ophthalmologists' awareness, 66.6% after the second follow-up, and 25.4% among patients with myopia. After multivariate analysis, the following variables showed significant results: eye rubbing frequency more than 10 times/day (odds ratio [OR], 9.168; P < .001); rubbing with knuckles (OR, 9.804; P = .001); and prone sleep position (OR, 12.427; P < .001). The proportion of patients who rubbed their eyes with stage IV keratoconus was 71.9%, 18.9% higher than those with stage I, 4.8% higher than stage II, and 17.8% higher than stage III. CONCLUSIONS: The proportion of Chinese patients with keratoconus who rubbed their eyes was relatively high. The main reasons for the low proportions reported were lack of attention. Clinical attention should be paid to eye rubbing in patients with keratoconus who should be educated to avoid it. [J Refract Surg. 2023;39(10):712-718.].

N6-methyladenosine in 7SK small nuclear RNA underlies RNA polymerase II transcription regulation.

N6-methyladenosine (m6A) modifications play crucial roles in RNA metabolism. How m6A regulates RNA polymerase II (RNA Pol II) transcription remains unclear. We find that 7SK small nuclear RNA (snRNA), a regulator of RNA Pol II promoter-proximal pausing, is highly m6A-modified in non-small cell lung cancer (NSCLC) cells. In A549 cells, we identified eight m6A sites on 7SK and discovered methyltransferase-like 3 (METTL3) and alkB homolog 5 (ALKBH5) as the responsible writer and eraser. When the m6A-7SK is specifically erased by a dCasRx-ALKBH5 fusion protein, A549 cell growth is attenuated due to reduction of RNA Pol II transcription. Mechanistically, removal of m6A leads to 7SK structural rearrangements that facilitate sequestration of the positive transcription elongation factor b (P-TEFb) complex, which results in reduction of serine 2 phosphorylation (Ser2P) in the RNA Pol II C-terminal domain and accumulation of RNA Pol II in the promoter-proximal region. Taken together, we uncover that m6A modifications of a non-coding RNA regulate RNA Pol II transcription and NSCLC tumorigenesis.

Lung cancer screening adherence among people living with and without HIV: An analysis of an integrated health system in Florida, United States (2012-2021).

Although lung cancer is a leading cause of death among people living with HIV (PLWH), limited research exists characterizing real-world lung cancer screening adherence among PLWH. Our objective was to compare low-dose computed tomography (LDCT) adherence among PLWH to those without HIV treated at one integrated health system. Using the University of Florida's Health Integrated Data Repository (01/01/2012-10/31/2021), we identified PLWH with at least one LDCT procedure, using Current Procedural Terminology codes(S8032/G0297/71271). Lung cancer screening adherence was defined as a second LDCT based on the Lung Imaging Reporting and Data System (Lung-RADS®). Lung-RADS categories were extracted from radiology reports using a natural language processing system. PLWH were matched with 4 randomly selected HIV-negative patients based on (+/- 1 year) age, Lung-RADS category, and calendar year. Seventy-three PLWH and 292 matched HIV-negative adults with at least one LDCT were identified. PLWH were more likely to be male (66% vs.52%,p < 0.04), non-Hispanic Black (53% vs.23%,p < 0.001), and live in an area of high poverty (45% vs.31%,p < 0.001). PLWH were more likely to be diagnosed with lung cancer after first LDCT (8% vs.0%,p < 0.001). Seventeen percent of HIV-negative and 12% of PLWH were adherent to LDCT screenings. Only 25% of PLWH diagnosed with category 4A were adherent compared to 44% of HIV-negative. On multivariable analyses, those with older age (66-80 vs.50-64 years) and with either Medicaid, charity-based, or other government insurance (vs. Medicare) were less likely to be adherent to LDCT screenings. PLWH may have poorer adherence to LDCT compared to their HIV-negative counterparts.

Allostatic load and risk of all-cause, cancer-specific, and cardiovascular mortality in older cancer survivors: an analysis of the National Health and Nutrition Examination Survey 1999-2010.

Background: Allostatic load has been linked to an increased risk of death in various populations. However, to date, there is no research specifically investigating the effect of allostatic load on mortality in older cancer survivors. Aims: To investigate the association between allostatic load (AL) and mortality in older cancer survivors. Method: A total of 1,291 adults aged 60 years or older who survived for ≥1 year since cancer diagnoses were identified from the 1999-2010 National Health and Nutrition Examination Survey. AL was the exposure of interest incorporating 9 clinical measures/biomarkers; one point was added to AL if any of the measures/biomarkers exceeded the normal level. The sum of points was categorized as an ordinal variable to reflect low, moderate, and high AL. Our outcomes of interest were all-cause, cancer-specific, and cardiovascular disease (CVD)-specific mortality. Death was identified by linkage to the National Death Index. Multivariable Cox proportional hazards models were used to estimate adjusted hazard ratio (aHR) and 95% confidence intervals (CI) of mortality by AL category. Results: Overall, 53.6% of participants were male and 78.4% were white. The mean age of study participants at interview was 72.8 years (SD=7.1). A total of 546 participants died during the follow-up (median follow-up time: 8.0 years). Among them, 158 died of cancer and 106 died of cardiovascular events. Results from multivariable Cox proportional hazards models showed that higher ALS was positively associated with higher all-cause mortality (ALS=4-9 vs. ALS =0-1: aHR=1.52, 95% CI =1.17-1.98, p-trend<0.01) and higher cancer-specific mortality (ALS=4-9 vs. ALS =0-1: aHR=1.80, 95% CI =1.12-2.90, p-trend=0.01). The association between ALS and cardiovascular mortality was positive but non-significant (ALS=4-9 vs. ALS =0-1: aHR=1.59, 95% CI =0.86-2.94, p-trend=0.11). Conclusions: Our study suggests that older cancer survivors can have a higher risk of death if they have a high burden of AL.

Leveraging natural language processing to identify eligible lung cancer screening patients with the electronic health record.

OBJECTIVE: To develop and validate an approach that identifies patients eligible for lung cancer screening (LCS) by combining structured and unstructured smoking data from the electronic health record (EHR). METHODS: We identified patients aged 50-80 years who had at least one encounter in a primary care clinic at Vanderbilt University Medical Center (VUMC) between 2019 and 2022. We fine-tuned an existing natural language processing (NLP) tool to extract quantitative smoking information using clinical notes collected from VUMC. Then, we developed an approach to identify patients who are eligible for LCS by combining smoking information from structured data and clinical narratives. We compared this method with two approaches to identify LCS eligibility only using smoking information from structured EHR. We used 50 patients with a documented history of tobacco use for comparison and validation. RESULTS: 102,475 patients were included. The NLP-based approach achieved an F1-score of 0.909, and accuracy of 0.96. The baseline approach could identify 5,887 patients. Compared to the baseline approach, the number of identified patients using all structured data and the NLP-based algorithm was 7,194 (22.2 %) and 10,231 (73.8 %), respectively. The NLP-based approach identified 589 Black/African Americans, a significant increase of 119 %. CONCLUSION: We present a feasible NLP-based approach to identify LCS eligible patients. It provides a technical basis for the development of clinical decision support tools to potentially improve the utilization of LCS and diminish healthcare disparities.

Key Technologies of Pure Hydrogen and Hydrogen-Mixed Natural Gas Pipeline Transportation.

Thanks to the advantages of cleanliness, high efficiency, extensive sources, and renewable energy, hydrogen energy has gradually become the focus of energy development in the world's major economies. At present, the natural gas transportation pipeline network is relatively complete, while hydrogen transportation technology faces many challenges, such as the lack of technical specifications, high safety risks, and high investment costs, which are the key factors that hinder the development of hydrogen pipeline transportation. This paper provides a comprehensive overview and summary of the current status and development prospects of pure hydrogen and hydrogen-mixed natural gas pipeline transportation. Analysts believe that basic studies and case studies for hydrogen infrastructure transformation and system optimization have received extensive attention, and related technical studies are mainly focused on pipeline transportation processes, pipe evaluation, and safe operation guarantees. There are still technical challenges in hydrogen-mixed natural gas pipelines in terms of the doping ratio and hydrogen separation and purification. To promote the industrial application of hydrogen energy, it is necessary to develop more efficient, low-cost, and low-energy-consumption hydrogen storage materials.