Modeling Thyroid Cancer Epidemiology in the United States Using Papillary Thyroid Carcinoma Microsimulation Model.

OBJECTIVES: Thyroid cancer incidence increased over 200% from 1992 to 2018, whereas mortality rates had not increased proportionately. The increased incidence has been attributed primarily to the detection of subclinical disease, raising important questions related to thyroid cancer control. We developed the Papillary Thyroid Carcinoma Microsimulation model (PATCAM) to answer them, including the impact of overdiagnosis on thyroid cancer incidence. METHODS: PATCAM simulates individuals from age 15 until death in birth cohorts starting from 1975 using 4 inter-related components, including natural history, detection, post-diagnosis, and other-cause mortality. PATCAM was built using high-quality data and calibrated against observed age-, sex-, and stage-specific incidence in the United States as reported by the Surveillance, Epidemiology, and End Results database. PATCAM was validated against US thyroid cancer mortality and 3 active surveillance studies, including the largest and longest running thyroid cancer active surveillance cohort in the world (from Japan) and 2 from the United States. RESULTS: PATCAM successfully replicated age- and stage-specific papillary thyroid cancers (PTC) incidence and mean tumor size at diagnosis and PTC mortality in the United States between 1975 and 2015. PATCAM accurately predicted the proportion of tumors that grew more than 3 mm and 5 mm in 5 years and 10 years, aligning with the 95% confidence intervals of the reported rates from active surveillance studies in most cases. CONCLUSIONS: PATCAM successfully reproduced observed US thyroid cancer incidence and mortality over time and was externally validated. PATCAM can be used to identify factors that influence the detection of subclinical PTCs.

Predicting health-related quality of life change using natural language processing in thyroid cancer.

Background: Patient-reported outcomes (PRO) allow clinicians to measure health-related quality of life (HRQOL) and understand patients' treatment priorities, but obtaining PRO requires surveys which are not part of routine care. We aimed to develop a preliminary natural language processing (NLP) pipeline to extract HRQOL trajectory based on deep learning models using patient language. Materials and methods: Our data consisted of transcribed interviews of 100 patients undergoing surgical intervention for low-risk thyroid cancer, paired with HRQOL assessments completed during the same visits. Our outcome measure was HRQOL trajectory measured by the SF-12 physical and mental component scores (PCS and MCS), and average THYCA-QoL score.We constructed an NLP pipeline based on BERT, a modern deep language model that captures context semantics, to predict HRQOL trajectory as measured by the above endpoints. We compared this to baseline models using logistic regression and support vector machines trained on bag-of-words representations of transcripts obtained using Linguistic Inquiry and Word Count (LIWC). Finally, given the modest dataset size, we implemented two data augmentation methods to improve performance: first by generating synthetic samples via GPT-2, and second by changing the representation of available data via sequence-by-sequence pairing, which is a novel approach. Results: A BERT-based deep learning model, with GPT-2 synthetic sample augmentation, demonstrated an area-under-curve of 76.3% in the classification of HRQOL accuracy as measured by PCS, compared to the baseline logistic regression and bag-of-words model, which had an AUC of 59.9%. The sequence-by-sequence pairing method for augmentation had an AUC of 71.2% when used with the BERT model. Conclusions: NLP methods show promise in extracting PRO from unstructured narrative data, and in the future may aid in assessing and forecasting patients' HRQOL in response to medical treatments. Our experiments with optimization methods suggest larger amounts of novel data would further improve performance of the classification model.

Telemedicine Use across Medical Specialties and Diagnoses.

BACKGROUND: The COVID-19 (coronavirus disease 2019) pandemic rapidly expanded telemedicine scale and scope. As telemedicine becomes routine, understanding how specialty and diagnosis combine with demographics to impact telemedicine use will aid in addressing its current limitations. OBJECTIVES: To analyze the relationship between medical specialty, diagnosis, and telemedicine use, and their interplay with patient demographics in determining telemedicine usage patterns. METHODS: We extracted encounter and patient data of all adults who scheduled outpatient visits from June 1, 2020 to June 30, 2021 from the electronic health record of an integrated academic health system encompassing a broad range of subspecialties. Extracted variables included medical specialty, primary visit diagnosis, visit modality (video, audio, or in-person), and patient age, sex, self-reported race/ethnicity and 2013 rural-urban continuum code. Six specialties (General Surgery, Family Medicine, Gastroenterology, Oncology, General Internal Medicine, and Psychiatry) ranging from the lowest to the highest quartile of telemedicine use (video and audio) were chosen for analysis. Relative proportions of video, audio, and in-person modalities were compared. We examined diagnoses associated with the most and least frequent telemedicine use within each specialty. Finally, we analyzed associations between patient characteristics and telemedicine modality (video vs. audio/in-person, and video/audio vs. in-person) using a mixed-effects logistic regression model. RESULTS: A total of 2,494,296 encounters occurred during the study period, representing 420,876 unique patients (mean age: 44 years, standard deviation: 24 years, 54% female). Medical diagnoses requiring physical examination or minor procedures were more likely to be conducted in-person. Rural patients were more likely than urban patients to use video telemedicine in General Surgery and Gastroenterology and less likely to use video for all other specialties. Within most specialties, male patients and patients of nonwhite race were overall less likely to use video modality and video/audio telemedicine. In Psychiatry, members of several demographic groups used video telemedicine more commonly than expected, while in other specialties, members of these groups tended to use less telemedicine overall. CONCLUSION: Medical diagnoses requiring physical examination or minor procedures are more likely to be conducted in-person. Patient characteristics (age, sex, rural vs. urban, race/ethnicity) affect video and video/audio telemedicine use differently depending on medical specialty. These factors contribute to a unique clinical scenario which impacts perceived usefulness and accessibility of telemedicine to providers and patients, and are likely to impact rates of telemedicine adoption.

A comparison of NSQIP and CESQIP in data quality and ability to predict thyroidectomy outcomes.

BACKGROUND: The Collaborative Endocrine Surgery Quality Improvement Program tracks thyroidectomy outcomes with self-reported data, whereas the National Surgical Quality Improvement Program uses professional abstractors. We compare completeness and predictive ability of these databases at a single-center and national level. METHOD: Data consistency in the Collaborative Endocrine Surgery Quality Improvement Program and the National Surgical Quality Improvement Program at a single institution (2013-2020) was evaluated using McNemar's test. At the national level, data from the Collaborative Endocrine Surgery Quality Improvement Program and the National Surgical Quality Improvement Program (2016-2019) were used to compare predictive capability for 4 outcomes within each data source: thyroidectomy-specific complication, systemic complication, readmission, and reoperation, as measured by area under curve. RESULTS: In the single-center analysis, 66 cases were recorded in both the Collaborative Endocrine Surgery Quality Improvement Program and the National Surgical Quality Improvement Program. The reoperation variable had the most discrepancies (2 vs 0 in the National Surgical Quality Improvement Program versus the Collaborative Endocrine Surgery Quality Improvement Program, respectively; χ2 = 2.00, P = .16). At the national level, there were 24,942 cases in the National Surgical Quality Improvement Program and 17,666 cases in the Collaborative Endocrine Surgery Quality Improvement Program. In the National Surgical Quality Improvement Program, 30-day thyroidectomy-specific complication, systemic complication, readmission, and reoperation were 13.25%, 2.13%, 1.74%, and 1.39%, respectively, and in the Collaborative Endocrine Surgery Quality Improvement Program 7.27%, 1.95%, 1.64%, and 0.81%. The area under curve of the National Surgical Quality Improvement Program was higher for predicting readmission (0.721 [95% confidence interval 0.703-0.737] vs 0.613 [0.581-0.649]); the area under curve of the Collaborative Endocrine Surgery Quality Improvement Program was higher for thyroidectomy-specific complication (0.724 [0.708-0.737] vs 0.677 [0.667-0.687]) and reoperation (0.735 [0.692-0.775] vs 0.643 [0.611-0.673]). Overall, 3.44% vs 27.22% of values were missing for the National Surgical Quality Improvement Program and the Collaborative Endocrine Surgery Quality Improvement Program, respectively. CONCLUSION: The Collaborative Endocrine Surgery Quality Improvement Program was more accurate in predicting thyroidectomy-specific complication and reoperation, underscoring its role in collecting granular, disease-specific variables. However, a higher proportion of data are missing. The National Surgical Quality Improvement Program infrastructure leads to more rigorous data capture, but the Collaborative Endocrine Surgery Quality Improvement Program is better at predicting thyroid-specific outcomes.

Diagnostic Accuracy of Fine-Needle Biopsy in the Detection of Thyroid Malignancy: A Systematic Review and Meta-analysis.

Importance: Fine-needle biopsy (FNB) became a critical part of thyroid nodule evaluation in the 1970s. It is not clear how diagnostic accuracy of FNB has changed over time. Objective: To conduct a systematic review and meta-analysis estimating the accuracy of thyroid FNB for diagnosis of malignancy in adults with a newly diagnosed thyroid nodule and to characterize changes in accuracy over time. Data Sources: PubMed, SCOPUS, and Cochrane Central Register of Controlled Trials were searched from 1975 to 2020 using search terms related to FNB accuracy in the thyroid. Study Selection: English-language reports of cohort studies or randomized trials of adult patients undergoing thyroid FNB with sample size of 20 or greater and using a reference standard of surgical histopathology or clinical follow-up were included. Articles that examined only patients with known thyroid disease or focused on accuracy of novel adjuncts, such as molecular tests, were excluded. Two investigators screened each article and resolved conflicts by consensus. A total of 36 of 1023 studies met selection criteria. Data Extraction and Synthesis: The MOOSE guidelines were used for data abstraction and assessing data quality and validity. Two investigators abstracted data using a standard form. Studies were grouped into epochs by median data collection year (1975 to 1990, 1990 to 2000, 2000 to 2010, and 2010 to 2020). Data were pooled using a bivariate mixed-effects model. Main Outcomes and Measures: The primary outcome was accuracy of FNB for diagnosis of malignancy. Accuracy was hypothesized to increase in later time periods, a hypothesis formulated prior to data collection. Results: Of 16 597 included patients, 12 974 (79.2%) were female, and the mean (SD) age was 47.3 (12.9) years. The sensitivity of FNB was 85.6% (95% CI, 79.9-89.5), the specificity was 71.4% (95% CI, 61.1-79.8), the positive likelihood ratio was 3.0 (95% CI, 2.3-4.1), and the negative likelihood ratio was 0.2 (95% CI, 0.2-0.3). The area under the receiver operating characteristic curve was 86.1%. Epoch was not significantly associated with accuracy. None of the available covariates could explain observed heterogeneity. Conclusions and Relevance: Accuracy of thyroid FNB has not significantly changed over time. Important developments in technique, preparation, and interpretation may have occurred too heterogeneously to capture a consistent uptrend over time. FNB remains a reliable test for thyroid cancer diagnosis.

Prevalence of Subclinical Papillary Thyroid Cancer by Age: Meta-analysis of Autopsy Studies.

CONTEXT: It is not known how underlying subclinical papillary thyroid cancer (PTC) differs by age. This meta-analysis of autopsy studies investigates how subclinical PTC prevalence changes over the lifetime. METHODS: We searched PubMed, Embase, and Web of Science databases from inception to May 2021 for studies that reported the prevalence of PTC found at autopsy. Two investigators extracted the number of subclinical PTCs detected in selected age groups and extent of examination. A quality assessment tool was used to assess bias. Logistic regression models with random intercepts were used to pool the age-specific subclinical PTC prevalence estimates. RESULTS: Of 1773 studies screened, 16 studies with age-specific data met the inclusion criteria (n = 6286 autopsies). The pooled subclinical PTC prevalence was 12.9% (95% CI 7.8-16.8) in whole gland and 4.6% (2.5- 6.6) in partial gland examination. Age-specific prevalence estimates were ≤40 years, 11.5% (6.8-16.1); 41-60 years, 12.1% (7.6-16.5); 61-80 years, 12.7% (8-17.5); and 81+ years, 13.4% (7.9-18.9). Sex did not affect age-specific prevalence and there was no difference in prevalence between men and women in any age group. In the regression model, the OR of prevalence increasing by age group was 1.06 (0.92-1.2, P = .37). CONCLUSION: This meta-analysis shows the prevalence of subclinical PTC is stable across the lifespan. There is not a higher subclinical PTC prevalence in middle age, in contrast to higher observed incidence rates in this age group. These findings offer unique insights into the prevalence of subclinical PTC and its relationship to age.

Complication Rates of Total Thyroidectomy vs Hemithyroidectomy for Treatment of Papillary Thyroid Microcarcinoma: A Systematic Review and Meta-analysis.

Importance: Papillary thyroid microcarcinomas (PTMCs) have been associated with increased thyroid cancer incidence in recent decades. Total thyroidectomy (TT) has historically been the primary treatment, but current guidelines recommend hemithyroidectomy (HT) for select low-risk cancers; however, the risk-benefit ratio of the 2 operations is incompletely characterized. Objective: To compare surgical complication rates between TT and HT for PTMC treatment. Data Sources: SCOPUS, Medline via the PubMed interface, and the Cochrane Central Register of Controlled Trials (CENTRAL); through January 1, 2021, with no starting date restriction. Terms related to papillary thyroid carcinoma and its treatment were used for article retrieval. This meta-analysis used the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guideline and was written according to the Meta-analysis of Observational Studies in Epidemiology (MOOSE) proposal. Study Selection: Original investigations of adults reporting primary surgical treatment outcomes in PTMC and at least 1 complication of interest were included. Articles evaluating only secondary operations or non-open surgical approaches were excluded. Study selection, data extraction, and risk of bias assessment were performed by 2 independent reviewers and conflicts resolved by a senior reviewer. Data Extraction and Synthesis: Pooled effect estimates were calculated using a random-effects inverse-variance weighting model. Main Outcomes and Measures: Cancer recurrence and site, mortality (all-cause and disease-specific), vocal fold paralysis, hypoparathyroidism, and hemorrhage/hematoma. Risk of bias was assessed using the McMaster Quality Assessment Scale of Harms scale. Results: In this systematic review and meta-analysis, 17 studies were analyzed and included 1416 patients undergoing HT and 2411 patients undergoing TT (HT: pooled mean [SD] age, 47.0 [10.0] years; 1139 [84.6%] were female; and TT: pooled mean [SD] age, 48.8 [10.0] years; 1671 [77.4%] were female). Patients undergoing HT had significantly lower risk of temporary vocal fold paralysis compared with patients undergoing TT (3.3% vs 4.5%) (weighted risk ratio [RR], 0.4; 95% CI, 0.2-0.7), temporary hypoparathyroidism (2.2% vs 21.3%) (weighted RR, 0.1; 95% CI, 0.0-0.4), and permanent hypoparathyroidism (0% vs 1.8%) (weighted RR, 0.2; 95% CI, 0.0-0.8). Contralateral lobe malignant neoplasm recurrence was 2.3% in the HT group, while no such events occurred in the TT group. Hemithyroidectomy was associated with a higher overall recurrence rate (3.8% vs 1.0%) (weighted RR, 2.6; 95% CI, 1.3-5.4), but there was no difference in recurrence in the thyroid bed or neck. Conclusions and Relevance: The results of this systematic review and meta-analysis help characterize current knowledge of the risk-benefit ratio of HT vs TT for treatment of PTMC and provide data that may have utility for patient counseling surrounding treatment decisions.

Risk Stratifying Indeterminate Thyroid Nodules With Machine Learning.

BACKGROUND: Up to 30% of thyroid nodules are classified as indeterminate after fine needle aspiration biopsy. These indeterminate thyroid nodules (ITNs) require surgical pathology for definitive diagnosis. Molecular testing provides additional pre-operative cancer risk stratification but adds expense and invasive testing. The purpose of this study is to utilize a machine learning (ML) algorithm to predict malignancy of ITNs using data available from less invasive tests. MATERIALS AND METHODS: We conducted a retrospective study using medical records from one academic and one community center. Thyroid nodules with an indeterminate diagnosis on fine needle aspiration biopsy and completed diagnostic pathology were included. Linear, non-linear, and non-linear-ensemble ML methods were tested for accuracy when predicting malignancy using 10-fold cross-validation. Classifiers were evaluated using area under the receiver operating characteristics curve (AUROC). RESULTS: A total of 355 nodules met inclusion criteria. Of these, 171 (48.2%) were diagnosed with cancer. A Random Forest classifier performed the best, producing an accuracy of 79.1%, a sensitivity of 75.5%, specificity of 82.4%, positive predicative value of 80.3%, negative predictive value of 79.0%, and an AUROC of 0.859. CONCLUSIONS: ML methods accurately risk stratify ITNs using data gathered from existing, non-invasive, and inexpensive diagnostic tests. Applying an ML model with existing data can become a cost-effective alternative to molecular testing. Future studies will prospectively evaluate the performance of this ML approach when combined with expert judgment.

Evaluating Discrimination of ACS-NSQIP Surgical Risk Calculator in Thyroidectomy Patients.

BACKGROUND: The ACS-NSQIP surgical risk calculator (SRC) often guides preoperative counseling, but the rarity of complications in certain populations causes class imbalance, complicating risk prediction. We aimed to compare the performance of the ACS-NSQIP SRC to other classical machine learning algorithms trained on NSQIP data, and to demonstrate challenges and strategies in predicting such rare events. METHODS: Data from the NSQIP thyroidectomy module ys 2016 - 2018 were used to train logistic regression, Ridge regression and Random Forest classifiers for predicting 2 different composite outcomes of surgical risk (systemic and thyroidectomy-specific). We implemented techniques to address imbalanced class sizes and reported the area under the receiver operating characteristic (AUC) for each classifier including the ACS-NSQIP SRC, along with sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) at a 5% - 15% predicted risk threshold. RESULTS: Of 18,078 included patients, 405 (2.24%) patients suffered systemic complications and 1670 (9.24%) thyroidectomy-specific complications. Logistic regression performed best for predicting systemic complication risk (AUC 0.723 [0.658 - 0.778]); Random Forest with RUSBoost performed best for predicting thyroidectomy-specific complication risk (0.702; 0.674 - 0.726). The addition of optimizations for class imbalance improved performance for all classifiers. CONCLUSIONS: Complications are rare after thyroidectomy even when considered as composite outcomes, and class imbalance poses a challenge in surgical risk prediction. Using the SRC as a classifier where intervention occurs above a certain validated threshold, rather than citing the numeric estimates of complication risk, should be considered in low-risk patients.

False Negative Rates in Benign Thyroid Nodule Diagnosis: Machine Learning for Detecting Malignancy.

BACKGROUND: Thyroid nodules are common; up to 67% of adults will show nodules on high-quality ultrasound, and 95% of these nodules are benign. FNA cytology is a crucial step in determining the risk of malignancy, and a false negative diagnosis at this stage delays cancer treatment. The purpose of this study is to develop a predictive model using machine learning which can identify false negative FNA results based on less-invasive clinical data. MATERIALS AND METHODS: We conducted a retrospective medical record review at one academic and one community center. Inclusion criteria were thyroid nodules evaluated by ultrasound and FNA with a Bethesda II (benign) result or malignancy detected on pathology or FNA. Linear, non-linear, and ensemble models were generated with scikit-learn using 10-fold cross validation with repetition and compared with AUROC. The classification task was the prediction of malignancy using information acquired from less-invasive ultrasound and FNA. RESULTS: A total of 604 subjects met inclusion criteria; 38 were diagnosed with malignancy. Of all algorithms tested, a Random Forest method achieved the best AUROC (0.64) in separating benign and malignant nodules, though the improvement over other tested algorithms was not statistically significant. CONCLUSIONS: A Random Forest model performed better than random chance using readily available data obtained via standard evaluation of thyroid nodules. The diagnostic probability threshold of this model can be varied to minimize false positives at the cost of increasing the number of false negatives. Future studies will prospectively evaluate the model's performance.

Disparities in Telemedicine Access: A Cross-Sectional Study of a Newly Established Infrastructure during the COVID-19 Pandemic.

BACKGROUND: The COVID-19 pandemic led to dramatic increases in telemedicine use to provide outpatient care without in-person contact risks. Telemedicine increases options for health care access, but a "digital divide" of disparate access may prevent certain populations from realizing the benefits of telemedicine. OBJECTIVES: The study aimed to understand telemedicine utilization patterns after a widespread deployment to identify potential disparities exacerbated by expanded telemedicine usage. METHODS: We performed a cross-sectional retrospective analysis of adults who scheduled outpatient visits between June 1, 2020 and August 31, 2020 at a single-integrated academic health system encompassing a broad range of subspecialties and a large geographic region in the Upper Midwest, during a period of time after the initial surge of COVID-19 when most standard clinical services had resumed. At the beginning of this study period, approximately 72% of provider visits were telemedicine visits. The primary study outcome was whether a patient had one or more video-based visits, compared with audio-only (telephone) visits or in-person visits only. The secondary outcome was whether a patient had any telemedicine visits (video-based or audio-only), compared with in-person visits only. RESULTS: A total of 197,076 individuals were eligible (average age = 46 years, 56% females). Increasing age, rural status, Asian or Black/African American race, Hispanic ethnicity, and self-pay/uninsured status were significantly negatively associated with having a video visit. Digital literacy, measured by patient portal activation status, was significantly positively associated with having a video visit, as were Medicaid or Medicare as payer and American Indian/Alaskan Native race. CONCLUSION: Our findings reinforce previous evidence that older age, rural status, lower socioeconomic status, Asian race, Black/African American race, and Hispanic/Latino ethnicity are associated with lower rates of video-based telemedicine use. Health systems and policies should seek to mitigate such barriers to telemedicine when possible, with efforts such as digital literacy outreach and equitable distribution of telemedicine infrastructure.

Obesity May Not be Protective in Abdominal Stab Wounds.

CONTEXT: Current protocols for the management of abdominal stab wounds were established based on retrospective data from prior decades. Few have investigated whether higher body mass index (BMI) affects outcomes after these injuries. AIM: The aim was to determine the effects of obesity on outcomes in abdominal stab wound patients. SETTING AND DESIGN: This was a retrospective cohort study at a Level I university-associated trauma center in the United States. MATERIALS AND METHODS: We reviewed medical records of 100 adult patients admitted to our trauma center with abdominal stab wounds. Demographics, types of internal organ injury, gastrointestinal (GI) resection and repair, mortality, length of hospital stay (LOS), units of blood transfused within 24 h of admission, need and indications for exploratory laparotomy, surgical site infections (SSI), and need for re-operation were compared between obese and nonobese patients. STATISTICAL ANALYSIS: Categorical and continuous outcome variables were compared between the two groups using Chi-squared and independent-samples t-tests, respectively. BMI was evaluated as a predictor of outcomes using univariate and multivariate logistic regression. RESULTS: Records of 100 adult abdominal stab wound patients were reviewed. Twenty-five patients were obese. The obese group was older (38.76 vs. 31.23, P = 0.018). Rates of therapeutic laparotomy were similar between obese and nonobese patients (20 [80.00%] vs. 64 [85.33%]). Obesity was associated with longer LOS (9.6 vs. 6.5, P = 0.026). In the multivariate analysis, increasing BMI was an independent predictor of need for GI resection (odds ratio: 1.10 [1.02-1.18], P = 0.018). One patient from the obese group died. CONCLUSIONS: Obese patients with abdominal stab wounds have longer LOS than nonobese patients. Increasing BMI was an independent predictor of need for GI resection.

Sustained adenosine exposure causes lung endothelial apoptosis: a possible contributor to cigarette smoke-induced endothelial apoptosis and lung injury.

Pulmonary endothelial cell (EC) apoptosis has been implicated in the pathogenesis of emphysema. Cigarette smoke (CS) causes lung EC apoptosis and emphysema. In this study, we show that CS exposure increased lung tissue adenosine levels in mice, an effect associated with increased lung EC apoptosis and the development of emphysema. Adenosine has a protective effect against apoptosis via adenosine receptor-mediated signaling. However, sustained elevated adenosine increases alveolar cell apoptosis in adenosine deaminase-deficient mice. We established an in vitro model of sustained adenosine exposure by incubating lung EC with adenosine in the presence of an adenosine deaminase inhibitor, deoxycoformicin. We demonstrated that sustained adenosine exposure caused lung EC apoptosis via nucleoside transporter-facilitated intracellular adenosine uptake, subsequent activation of p38 and JNK in mitochondria, and ultimately mitochondrial defects and activation of the mitochondria-mediated intrinsic pathway of apoptosis. Our results suggest that sustained elevated adenosine may contribute to CS-induced lung EC apoptosis and emphysema. Our data also reconcile the paradoxical effects of adenosine on apoptosis, demonstrating that prolonged exposure causes apoptosis via nucleoside transporter-mediated intracellular adenosine signaling, whereas acute exposure protects against apoptosis via activation of adenosine receptors. Inhibition of adenosine uptake may become a new therapeutic target in treatment of CS-induced lung diseases.

Sustained adenosine exposure causes lung endothelial barrier dysfunction via nucleoside transporter-mediated signaling.

Previous studies by our group as well as others have shown that acute adenosine exposure enhances lung vascular endothelial barrier integrity and protects against increased permeability lung edema. In contrast, there is growing evidence that sustained adenosine exposure has detrimental effects on the lungs, including lung edema. It is well established that adenosine modulates lung inflammation. However, little is known concerning the effect of sustained adenosine exposure on lung endothelial cells (ECs), which are critical to the maintenance of the alveolar-capillary barrier. We show that exogenous adenosine plus adenosine deaminase inhibitor caused sustained elevation of adenosine in lung ECs. This sustained adenosine exposure decreased EC barrier function, elevated cellular reactive oxygen species levels, and activated p38, JNK, and RhoA. Inhibition of equilibrative nucleoside transporters (ENTs) prevented sustained adenosine-induced p38 and JNK activation and EC barrier dysfunction. Inhibition of p38, JNK, or RhoA also partially attenuated sustained adenosine-induced EC barrier dysfunction. These data indicate that sustained adenosine exposure causes lung EC barrier dysfunction via ENT-dependent intracellular adenosine uptake and subsequent activation of p38, JNK, and RhoA. The antioxidant N-acetylcysteine and the NADPH inhibitor partially blunted sustained adenosine-induced JNK activation but were ineffective in attenuation of p38 activation or barrier dysfunction. p38 was activated exclusively in mitochondria, whereas JNK was activated in mitochondria and cytoplasm by sustained adenosine exposure. Our data further suggest that sustained adenosine exposure may cause mitochondrial oxidative stress, leading to activation of p38, JNK, and RhoA in mitochondria and resulting in EC barrier dysfunction.