bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract C052: Endometrial cancer genomics varies by race
Authors: Sanchez-Covarrubias Alex P. , Sowamber Ramlogan , Dodds Leah , Pinto Andre , George Sophia , Schlumbrecht Matthew 
Published: January 2023

Abstract
Introduction Variability in endometrial cancer (EC) risk and outcomes exist, with Black women disproportionately negatively affected related to White women. The biologic etiologies of these observations are poorly understood. Our objective was to describe the somatic mutations of the tumor genome and compare them to RNA expression in self-identified Black and White women diagnosed with EC. Methods Women with newly diagnosed EC and undergoing hysterectomy were recruited prospectively. Fresh tumors obtained at the time of surgery were flash-frozen, underwent DNA and RNA extraction and were submitted for whole exome sequencing (WES) analysis (DNA) or bulk RNA sequencing analysis. Differentially expressed genes between racial groups and tumor grade were calculated using Deseq2. Genomic ancestry was determined using ADMIXTURE. Chi-square and Mann Whitney U tests were used for analyses, with a threshold for significance of p&lt;0.05. Results A total of 49 patients with EC were included. Thirty (61.2%) women were diagnosed with low-grade EC and 19 (38.8%) with high grade EC. Twenty (40.8%) women self-identified as Black, 27 (55.1%) as White and 2 (4.1%) as Asian/other. Admixture analysis showed that 36 (73.4%) had &gt;14% African ancestry. The remaining 13 (26.5%) patients had admixed European and Native American ancestry, with &lt;1% African ancestry. In the RNA-seq, we found significant DEGs by tumor grade; with 2132 DEG in High grade compared to Low grade tumors with a false discovery rate of &lt;0.05 and a fold change (FC) of ≤-1.5 or ≥1.5. Top 10 DEG included MT4, TAF15, SLFN5 and PLEKHS1 (upregulated) and MCRIP1, MAZ, MYOM3, PRPH, BCL7C, AC016700.5 (downregulated). In the WES analysis, 8 Black women had mutations in GON4L compared to 1 in the White woman (44.4% vs 3.7%, padj=0.04); and 7 Black women had a mutation in PRAMEF14 compared to 1 White woman (38.8% vs 3.7%, padj= 0.06). Conclusion GON4L, which is a transcription factor associated with decreased apoptosis and promotion of tumor growth through CD24 regulation, was significantly more mutated in the EC genome of Black women relative to White women. Somatic mutation and transcriptional changes are seen between tumor grade and self-identified race and require further exploration for its relationship to aggressive disease.
Citation Format: Alex P. Sanchez-Covarrubias, Ramlogan Sowamber, Leah Dodds, Andre Pinto, Sophia George, Matthew Schlumbrecht. Endometrial cancer genomics varies by race [abstract]. In: Proceedings of the 15th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2022 Sep 16-19; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr C052.
Abstract C058: Deregulated microRNA-mRNA reciprocal pairs as novel precision biomarkers and therapeutic targets for African American prostate cancer
Authors: Gujrati Himali , Ha Siyoung , Wang Bi-Dar 
Published: January 2023

Abstract
Prostate cancer (PCa) is the most frequently diagnosed cancer and second leading cause of cancer death among American men. Notably, African American (AA) men exhibit 1.6-fold higher incidence rate and 2.4-fold higher mortality rate when compared to European American (EA) men. Besides socioeconomic factors, accumulating genomic data suggest that intrinsic genetic risk elements also play critical roles in promoting PCa disparities. MicroRNAs (miRNAs) are involved in epigenetic mechanisms regulating mRNA expression in various human diseases, including cancers. In this study, we have applied a systems biology approach (by integrating mRNA and miRNA profiling data, target prediction, and pathway analysis) to identify critical signaling networks differentially regulated between AA and EA PCa. Using miRNA-drive algorithm mirPath V.3 coupled with mRNA mapping, and global test algorithm coupled with miRNA mapping, we identified several cancer pathways, such as ERBB, mTOR, HIF, and VEGF signaling, that are significantly regulated by AA-depleted/enriched miRNAs and mRNAs. Specifically, reciprocal miRNA-mRNA pairs miR-34a-5p/HIF1A, miR-34a-5p/PIK3CB, miR-34a-5p/IGFBP2, and miR-99b-5p/MTOR (downregulated/upregulated in AA vs. EA PCa), and miR-96-5p/MAPKAPK2 (upregulated/downregulated in AA vs. EA PCa) were identified as core miRNA/mRNA regulators in activation of mTOR and VEGF signaling in AA PCa. RT-qPCR, Western blotting, and immunohistochemistry assays in AA and EA PCa cell lines and patient samples have confirmed that these miRNAs and mRNAs are differentially expressed in AA and EA PCa. Furthermore, molecular targeting miRNA/mRNA pairings using miR-34a-5p, miR-99b-5p mimics and/or miR-96-5p antagomir consequently reduced cell proliferation and enhanced docetaxel-induced cytotoxicity in AA PCa cells. In summary, these results suggest that deregulated miRNA/mRNA reciprocal pairs play critical roles in promoting cancer aggressiveness and chemoresistance in AA PCa. Further development of these miRNA/mRNA pairings as precision diagnostic/prognostic biomarkers and novel therapeutic targets may pave a new path for PCa diagnosis and treatment.
Citation Format: Himali Gujrati, Siyoung Ha, Bi-Dar Wang. Deregulated microRNA-mRNA reciprocal pairs as novel precision biomarkers and therapeutic targets for African American prostate cancer [abstract]. In: Proceedings of the 15th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2022 Sep 16-19; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr C058.
Abstract IA045: Bio-behavioral approaches to reducing racial disparities in obesity and GI cancers
Authors: Carson Tiffany L. 
Published: January 2023

Abstract
Obesity is associated with increased risk for all major gastrointestinal cancers. These chronic conditions are hypothesized to be linked via inflammation and/or hormones. Racial disparities persist for obesity with nearly 50% of non-Hispanic Black adults and 45.6% of Hispanic adults having obesity compared to 41.4% of non-Hispanic White adults. Similarly, Black and Hispanic individuals are at increased risk of developing and/or dying from some GI cancers compared to White individuals. Interventions targeting diet and physical activity offer one approach to reducing obesity and risk for developing GI cancers by reducing adiposity and via physiologic processes (e.g. modification of the gut microbiota) to promote a less carcinogenic environment in the GI tract. For example, dietary modifications can lead to weight loss and also modify the gut microbiota to lower risk for colorectal cancer. Previous research from our team has also identified racial differences in the gut microbiota suggesting that the gut microbiota is a viable target for intervention to reduce racial disparities in obesity and GI cancers. Strategies to reduce disparities in obesity and cancer using bio-behavioral approaches that focus on diet and the gut microbiota will be discussed.
Citation Format: Tiffany L. Carson. Bio-behavioral approaches to reducing racial disparities in obesity and GI cancers [abstract]. In: Proceedings of the 15th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2022 Sep 16-19; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr IA045.
TBX3 Promotes Cervical Cancer Proliferation and Migration via HPV E6 and E7 Signaling
Authors: Khan Saif F. , Burmeister Carly A. , Scott David J. , Sinkala Musalula , Ramburan Amsha , Wu Hue-Tsi , Schäfer Georgia , Katz Arieh A. , Prince Sharon 
Published: January 2023

AbstractCervical cancer is a leading cause of cancer-related deaths in women globally and 99% of cases are caused by persistent infection with high-risk strains of the human papillomavirus (HPV). The HPV oncoproteins E6 and E7 establish the cancer phenotype by cooperating with host proteins and identifying them may have important therapeutic benefits. T-box transcription factor 3 (TBX3) is a critical developmental regulator, and when it is overexpressed postnatally, it contributes to several cancers, but little is known about its expression and role in cervical cancer. The current study shows that TBX3 is upregulated in cervical cancer cell lines as well as precancerous and cervical cancer patient tissue and is associated with larger and more invasive tumors. Knockdown and overexpression cell culture models show that TBX3 promotes HPV-positive cell proliferation, migration, and spheroid growth; however, TBX3 inhibits these processes in HPV-negative cells. Importantly, we show that the tumor promoting activity of TBX3 in cervical cancer is dependent on E6/E7.Implications:In summary, our study highlights the importance of TBX3 as a cooperating partner of E6/E7 in HPV-positive cervical cancer and identifies TBX3 as a potential therapeutic target to treat this neoplasm.
Subseasonal Prediction of Central European Summer Heatwaves with Linear and Random Forest Machine Learning Models
Authors: Weirich-Benet Elizabeth , Pyrina Maria , Jiménez-Esteve Bernat , Fraenkel Ernest , Cohen Judah , Domeisen Daniela I. V. 
Published: January 2023

Abstract
Heatwaves are extreme near-surface temperature events that can have substantial impacts on ecosystems and society. Early warning systems help to reduce these impacts by helping communities prepare for hazardous climate-related events. However, state-of-the-art prediction systems can often not make accurate forecasts of heatwaves more than two weeks in advance, which are required for advance warnings. We therefore investigate the potential of statistical and machine learning methods to understand and predict central European summer heatwaves on time scales of several weeks. As a first step, we identify the most important regional atmospheric and surface predictors based on previous studies and supported by a correlation analysis: 2-m air temperature, 500-hPa geopotential, precipitation, and soil moisture in central Europe, as well as Mediterranean and North Atlantic sea surface temperatures, and the North Atlantic jet stream. Based on these predictors, we apply machine learning methods to forecast two targets: summer temperature anomalies and the probability of heatwaves for 1–6 weeks lead time at weekly resolution. For each of these two target variables, we use both a linear and a random forest model. The performance of these statistical models decays with lead time, as expected, but outperforms persistence and climatology at all lead times. For lead times longer than two weeks, our machine learning models compete with the ensemble mean of the European Centre for Medium-Range Weather Forecast’s hindcast system. We thus show that machine learning can help improve subseasonal forecasts of summer temperature anomalies and heatwaves.

Significance Statement
Heatwaves (prolonged extremely warm temperatures) cause thousands of fatalities worldwide each year. These damaging events are becoming even more severe with climate change. This study aims to improve advance predictions of summer heatwaves in central Europe by using statistical and machine learning methods. Machine learning models are shown to compete with conventional physics-based models for forecasting heatwaves more than two weeks in advance. These early warnings can be used to activate effective and timely response plans targeting vulnerable communities and regions, thereby reducing the damage caused by heatwaves.
Creating and Evaluating Uncertainty Estimates with Neural Networks for Environmental-Science Applications
Authors: Haynes Katherine , Lagerquist Ryan , McGraw Marie , Musgrave Kate , Ebert-Uphoff Imme 
Published: January 2023

Abstract
Neural networks (NN) have become an important tool for prediction tasks—both regression and classification—in environmental science. Since many environmental-science problems involve life-or-death decisions and policy making, it is crucial to provide not only predictions but also an estimate of the uncertainty in the predictions. Until recently, very few tools were available to provide uncertainty quantification (UQ) for NN predictions. However, in recent years the computer-science field has developed numerous UQ approaches, and several research groups are exploring how to apply these approaches in environmental science. We provide an accessible introduction to six of these UQ approaches, then focus on tools for the next step, namely, to answer the question: Once we obtain an uncertainty estimate (using any approach), how do we know whether it is good or bad? To answer this question, we highlight four evaluation graphics and eight evaluation scores that are well suited for evaluating and comparing uncertainty estimates (NN based or otherwise) for environmental-science applications. We demonstrate the UQ approaches and UQ-evaluation methods for two real-world problems: 1) estimating vertical profiles of atmospheric dewpoint (a regression task) and 2) predicting convection over Taiwan based on Himawari-8 satellite imagery (a classification task). We also provide Jupyter notebooks with Python code for implementing the UQ approaches and UQ-evaluation methods discussed herein. This article provides the environmental-science community with the knowledge and tools to start incorporating the large number of emerging UQ methods into their research.

Significance Statement
Neural networks are used for many environmental-science applications, some involving life-or-death decision-making. In recent years new methods have been developed to provide much-needed uncertainty estimates for NN predictions. We seek to accelerate the adoption of these methods in the environmental-science community with an accessible introduction to 1) methods for computing uncertainty estimates in NN predictions and 2) methods for evaluating such estimates.
Relationship Between Serum Uric Acid and Carotid Plaque in Patients With Coronary Artery Disease by Sex and Blood Pressure Status
Authors: Pan Guangwei , Liu Yijia , Yu Lu , Yang Rongrong , Yang Tong , Wang Yang , Su Jinyu , Li Zhu , Cheng Qi , Gao Sheng , Li Lin , Yu Chunquan 
Published: January 2023

The purpose of this study was to explore the sex difference and effects of blood pressure (BP) on the relationship between serum uric acid (SUA) and carotid plaque in patients with coronary heart disease (CHD). This large multicenter retrospective study included 12099 patients with CHD (aged 35–75 years) between January 1, 2014 and September 30, 2020. Patients were divided into three groups according to systolic BP (SBP) and diastolic BP (DBP), and the SUA levels in males and females were converted into three groups. Logistic regression was used to analyze the influence of sex and BP on the relationship between SUA levels and carotid plaque in patients with CHD. In the model of male BP subgroups, using the BP of group A (normal with SBP <120 mmHg and DBP <80 mmHg) as a reference, SUA levels were significantly correlated with the occurrence of carotid plaque under different BP states ( P < .001). In contrast, in the model of female BP subgroups, most of these correlations were not statistically significant. Our study showed that SUA levels were significantly associated with carotid plaque occurrence in males with CHD, which remained significant across different BP states.
Validation of a screening algorithm for hepatic fibrosis by Doppler ultrasound and elastography in a general population
Authors: Renard Anne-Sophie , Paisant Anita , Cartier Victoire , Calès Paul , Goyet-Prelipcean Mirela , Geagea Edmond , Tasu Jean-Pierre , Silvain Christine , Wagner Mathilde , Cleach Aline Le , Vilgrain Valérie , Castera Laurent , Bricault Ivan , Decaens Thomas , Savoye-Collet Céline , Montialoux Hélène , Correas Jean-Michel , Vallet-Pichard Anaïs , Boursier Jérôme , Aubé Christophe 
Published: January 2023

Background Early detection can prevent the initial stages of fibrosis from progressing to cirrhosis. Purpose To evaluate an algorithm combining three echographic indicators and elastographic measurements to screen for hepatic fibrosis in an unselected population. Material and Methods From May 2017 to June 2018, all patients with no history and no known chronic liver disease who were referred for an ultrasound (US) were prospectively included in eight hospitals. The indicators being sought were liver surface irregularity, demodulation of hepatic veins, and spleen length >110 mm. Patients presenting at least one of these underwent elastography measurements with virtual touch quantification (VTQ) or supersonic shear imaging (SSI). If elastography was positive, patients were referred to hepatologist for fibrosis evaluation. Reference standard was obtained by FibroMeterVCTE or biopsy. A FibroMeterVCTE result >0.384 indicated a “necessary referral” to a hepatologist. Results Of the 1501 patients included, 504 (33.6%) were positive for at least one US indicator. All of them underwent US elastography, with 85 being positive. Of the patients, 58 (3.6%) had a consultation with a liver specialist: 21 had positive FibroMeterVCTE and nine had an indication of biopsy for suspicion of fibrosis. This screening algorithm made it possible to diagnose 1.6% of patients in our population with unknown fibrosis. Of the patients, 50% referred to the liver specialist were “necessary referrals.” Conclusion Our study suggests that three simple US indicators with no systematic elastographic measurement could be applied in day-to-day practice to look for hepatic fibrosis in an unsuspected population allowing relevant referrals to a hepatologist.
Degree of Accuracy With Which Deep Learning for Ultrasound Images Identifies Osteochondritis Dissecans of the Humeral Capitellum
Authors: Shinohara Issei , Yoshikawa Tomoya , Inui Atsuyuki , Mifune Yutaka , Nishimoto Hanako , Mukohara Shintaro , Kato Tatsuo , Furukawa Takahiro , Tanaka Shuya , Kusunose Masaya , Hoshino Yuichi , Matsushita Takehiko , Kuroda Ryosuke 
Published: January 2023

Background: Medical screening using ultrasonography (US) has been performed on young baseball players for early detection of osteochondritis dissecans (OCD) of the humeral capitellum. Deep learning (DL) and artificial intelligence (AI) techniques are widely adopted in the medical imaging research field. Purpose/Hypothesis: The purpose of this study was to calculate the diagnostic accuracy using DL for US images of OCD. We hypothesized that using DL for US imaging would improve the prediction accuracy of OCD. Study Design: Cohort study (Diagnosis); Level of evidence, 2. Methods: A total of 40 elbows (mean age of patients, 12.1 years) that were suspected of having OCD at a medical checkup and later confirmed by radiographs and magnetic resonance imaging were included in the study. The affected elbows were used as the OCD group and the contralateral elbows as the control group. From US videos, 100 images per elbow were captured from different angles, and 4000 images of the elbows were prepared for both groups. Of these, 80% were randomly selected by DL models and used as training data; the remaining were used as test data. Transfer learning was conducted using 3 pretrained DL models. The confusion matrix and the area under the receiver operating characteristic curve (AUC) were used to evaluate the model, and the visualization of the areas deemed important by the DL models was also performed. Furthermore, OCD regions were detected using an automatic image recognition model based on DL. Results: Classification of the OCD image by the DL model was performed; the best accuracy score was 0.87; the recall was 1.00. AUC was high for all DL models. Visualization of important features showed that AI predicted the presence of OCD by focusing on the irregularity or discontinuity of the surface of subchondral bone. In the detection of OCD task, the mean average precision was 0.83. Conclusion: The DL on US images identified OCD with high accuracy. The important features detected by the DL models correspond to the areas used by clinicians in screening the US images. The OCD was also detected with high accuracy using the object detection model. The AI model may be used in medical screening for OCD.
Programmed death-ligand 1 expression in diffuse large B-cell lymphoma is associated with poor prognosis
Authors: Al-Khateeb Esraa , Abbas Manal A. , Khader Majd B. , Sughayer Maher A. 
Published: January 2023

Background Programmed death-ligand 1 (PD-L1) expression in some tumors has prognostic implications. This work aims at investigating PD-L1 expression in diffuse large B-cell lymphoma (DLBCL) and to study its association with clinicopathological variables. Methods The study consisted of 75 DLBCL patients who were cared for at the King Hussein Cancer Center during the period 2015–2018. The expression of PD-L1 in tumor tissue was assessed by immunohistochemistry using the anti-human PD-L1 (Clone 22C3) monoclonal antibody. The correlation between gender, age, clinical stage, pre-treatment-LDH level, tumor location, response to therapy, overall and event-free survival with PD-L1 expression was studied. Results Six patients were excluded from further analysis as they were in relapse at the time of tissue sampling. The tumor proportion score (TPS) was ≥1% in 16/69 (23.2%) of DLBCL cases while the combined positive score (CPS) at a cut-off of ≥20 was observed in 23/69 (33.3%) cases. No significant difference in PD-L1 expression was found between germinal center B-cell-like (GCB) and non-GCB subtypes. Similarly, no differences in PD-L1 expression (at CPS ≥20 and TPS ≥1) were found between different genders, age groups, clinical stages, tumor location, and patient response to therapy. However, base-line lactate dehydrogenase was significantly elevated in patients with PD-L1 CPS ≥20. The overall survival was not significantly different between PD-L1-positive and -negative groups. On the other hand, the median event-free survival was higher in either of the PD-L1 TPS or CPS negative groups at 107months each versus 54 months in the PD-L1 positive group of either category. Conclusions PD-L1 expression can predict event-free survival in DLBCL cases and therefore poor prognosis.