Oakland score to identify low-risk patients with lower gastrointestinal bleeding performs well among emergency department patients by Daniel D. DiLena, Sean C. Bouvet, Madeline J. Somers, Maqdooda A. Merchant, Theodore R. Levin, Adina S. Rauchwerger, Dana R. Sax

“…Abstract Background The Oakland Score predicts risk of 30-day adverse events among hospitalized patients with lower gastrointestinal bleeding (LGIB) possibly identifying patients who may be safe for discharge. …”
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Complementary cognitive roles for D2-MSNs and D1-MSNs during interval timing by Robert A Bruce, Matthew Weber, Alexandra Bova, Rachael Volkman, Casey Jacobs, Kartik Sivakumar, Hannah Stutt, Youngcho Kim, Rodica Curtu, Kumar Narayanan

“…In line with this prediction, we found that optogenetic inhibition or pharmacological disruption of either D2-MSNs or D1-MSNs increased interval timing response times. …”
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A New Precursor Integral Method for Solving Space-Dependent Kinetic Equations in Neutronic and Thermal-Hydraulic Coupling System by Yingjie Wu, Baokun Liu, Han Zhang, Jiong Guo, Fu Li, Jinlin Niu, Yizhen Wang, Menglei Cui

“…The accurate prediction of the neutronic and thermal-hydraulic coupling system transient behavior is important in nuclear reactor safety analysis, where a large-scale nonlinear coupling system with strong stiffness should be solved efficiently. …”
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Contribution of dust emissions from farmland to particulate matter concentrations in North China Plain: Integration of WRF-Chem and WEPS model by Haopeng Zhang, Feng Wang, Shenghui Zhou, Tianning Zhang, Minghui Qi, Hongquan Song

“…This study integrated the Wind Erosion Prediction System (WEPS) model with the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to investigate the effect of agricultural dust emissions on PM10 concentrations in the NCP. …”
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The prognostic role of cardiac and inflammatory biomarkers in extubation failure in patients with COVID-19 acute respiratory distress syndrome by Carline N. L. Groenland, Adinde H. Siemers, Eric A. Dubois, Diederik Gommers, Leo Heunks, Evert-Jan Wils, Vivan J. M. Baggen, Henrik Endeman

“…The role of biomarkers in the prediction of extubation failure is currently limited. …”
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Spectrum-effect relationship of immunologic activity of Ganoderma lucidum by UPLC-MS/MS and component knock-out method by Changqin Li, Yiping Cui, Jie Lu, Lijun Meng, Changyang Ma, Zhenhua Liu, Yan Zhang, Wenyi Kang

“…HPLC fingerprint and spectrum-effect relationship of G. lucidum were established to predict the active compounds and BP neural network model was established to predict the efficacy. …”
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Comprehensive Assessment of the Risk of Symptomatic Embolism in Patients With Infective Endocarditis by Sebastian D. Santos‐Patarroyo, Juan A. Quintero‐Martinez, Brian D. Lahr, Supavit Chesdachai, Daniel C. DeSimone, Hector R. Villarraga, Hector I. Michelena, Larry M. Baddour

“…A nomogram that incorporated these factors was constructed to facilitate the prediction of embolic risk. Conclusions Highly mobile, larger vegetations are associated with embolic events. …”
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Grammar-constrained decoding for structured information extraction with fine-tuned generative models applied to clinical trial abstracts by David M. Schmidt, Philipp Cimiano

“…The addition of the pointer generators had a detrimental impact on the results, decreasing F1 scores by pp 0.15 (absolute score 0.263) and pp 0.198 (absolute score 0.272) for the best-performing pointer generator models on type 2 diabetes and glaucoma datasets, respectively.ConclusionThe experimental results indicate that encoder-decoder models used for structure prediction for information extraction tasks in low-resource settings clearly benefit from grammar-constrained decoding guiding the output generation. …”
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Aerodynamic energy consumption analysis of divided evacuated tube transportation system by Ziwei Zhang, Yingxue Wang, Guanqing Wang, Feilong Li, Dengke Wang, Jianjun Luo

“…By figuring out the spatiotemporal variation of tube gas, the theoretical energy estimation scheme for aerodynamic energy consumption with various cases was put forward, then the rationality and accuracy of which was verified by numerical simulation with prediction error less than 8%. The saving ratio of aerodynamic drag energy consumption of a 10 km tube could be 39.9% compared to traditional tunnels, which will be higher under ultra-long mileage, thus realising the overall aerodynamic energy saving effect. …”
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Estimated prevalence of post-intensive care cognitive impairment at short-term and long-term follow-ups: a proportional meta-analysis of observational studies by Mu-Hsing Ho, Yi-Wei Lee, Lizhen Wang

“…The pooled post-intensive care cognitive impairment prevalence rates at the follow-up timepoints < 1 month, 1–3 month(s), 4–6 months, 7–12 months, > 12 months were 49.8% [95% Prediction Interval (PI), 39.9%–59.7%, n = 19], 45.1% (95% PI, 34.8%–55.5%, n = 23), 47.9% (95% PI, 35.9%–60.0%, n = 16), 28.3% (95% PI, 19.9%–37.6%, n = 19), and 30.4% (95% PI, 18.4%–43.9%, n = 7), respectively. …”
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The causal relationship of cigarette smoking to metabolic disease risk and the possible mediating role of gut microbiota by Jingda Zhang, Lin Hou, Shanxiang Lei, Yan Li, Guogang Xu

“…Furthermore, to mitigate the impact of confounding factors, adjusted models were conducted via the multivariate Mendelian randomization (MVMR) method, aiming to improve the accuracy of prediction. Ultimately, the study evaluated the effect of the intermediary factor, gut microbiome, on the relationship between cigarette smoke and metabolic diseases. …”
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Establishment and validation of a nomogram for coronary artery lesions in children with Kawasaki disease by Chong Hu, Chong Hu, Xiao Yan, Henglian Song, Qin Dong, Qin Dong, Changying Yi, Changying Yi, Jianzhi Li, Jianzhi Li, Xin Lv, Xin Lv

“…BackgroundThe nomogram is a powerful and robust tool in disease risk prediction that summarizes complex variables into a visual model that is interpretable with a quantified risk probability. …”
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Development of data-driven algal bloom alert models with low temporal resolution data and application to Hong Kong rivers by Shujie Xu, Zhongnan Ye, Shu-Chien Hsu, Xiaoyi Liu, Chunmiao Zheng

“…This study offers a potential solution for prediction and forecasting without extensive datasets and emphasizes the importance of integrating domain knowledge in both prior and posterior analyses to enhance the interpretability of data-driven models in water science.…”
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Combined Treatment With Leuprolide Acetate and Burosumab in X-linked Hypophosphatemia and Precocious Puberty: A Therapeutic Response by Gustavo Tempone Cardoso Penna, MS, Carolina Costa Figueiredo, MD, MSc, Nara Michelle de Araújo Evangelista, MD, MSc, Vânia de Fátima Tonetto Fernandes, MD, PhD, Patricia Salmona, MD, Guido de Paula Colares Neto, MD, PhD

“…The combination of these treatments led to stabilized bone age, normalized growth velocity, and improved final height prediction without side effects or negative impacts on bone health during treatment. …”
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Characteristics and Associated Factors of Insomnia Among the General Population in the Post-Pandemic Era of COVID-19 in Zhejiang, China: A Cross-Sectional Study by Da M, Mou S, Hou G, Shen Z

“…Six machine learning models were employed to develop a predictive model for insomnia, namely logistic regression, random forest, neural network, support vector machine, CatBoost, and gradient boosting decision tree.Results: The study obtained 2769 and 1161 valid responses in T1 and T2, respectively. …”
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Single‐Cell Simultaneous Metabolome and Transcriptome Profiling Revealing Metabolite‐Gene Correlation Network by Xiying Mao, Dandan Xia, Miao Xu, Yan Gao, Le Tong, Chen Lu, Weiqi Li, Runmin Xie, Qinghuai Liu, Dechen Jiang, Songtao Yuan

“…However, the current single cell metabolome (SCM) study using mass spectroscopy has difficulty giving a complete view of the metabolic activity in the cell, and the prediction of the metabolism‐phenotype relationship is limited by the potential inconsistency between transcriptomic and metabolic levels. …”
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Seed Protein Content Estimation with Bench-Top Hyperspectral Imaging and Attentive Convolutional Neural Network Models by Imran Said, Vasit Sagan, Kyle T. Peterson, Haireti Alifu, Abuduwanli Maiwulanjiang, Abby Stylianou, Omar Al Akkad, Supria Sarkar, Noor Al Shakarji

“…This research aimed to develop robust methods for predicting seed protein concentration in wheat seeds using bench-top hyperspectral imaging in the visible, near-infrared (VNIR), and shortwave infrared (SWIR) regions. …”
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The effects of wind farm wakes on freezing sea spray in the mid-Atlantic offshore wind energy areas by D. Rosencrans, D. Rosencrans, J. K. Lundquist, J. K. Lundquist, J. K. Lundquist, M. Optis, M. Optis, N. Bodini

“…We analyze Weather Research and Forecasting model numerical weather prediction simulations at a coarse temporal resolution over a 21-year period to assess freezing sea spray (FSS) events over the long-term record and at finer granularity over the 2019–2020 winter season to identify the post-construction turbine impacts. …”
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Retrospective study of the frequency of diagnosing diffuse changes in the liver parenchyma according to the results of radiological studies by N.Yu. Ziuz, V.M. Bogomaz

“…To assess the quality of the model’s prediction, the area under the curve (AUC) and 95% CI were calculated. …”
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Protective effect and mechanism of Sufentanil on acute lung injury in septic mice by Hongqiao Hou, Bowen Jiang, Aiqing Zhu, Junjun Hou, Zhe Qu, Ruping Liu, Aiqun Li

“…To this end, a protein-protein interaction (PPI) network for common targets was first constructed with Swiss Target Prediction Database, GeneCards Database, Draw Venn Diagram Software, STRING 11.5 Database, Cytoscape 3.10.0 Software and Metascape Database, and then key targets were subject to enrichment analysis by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) to obtain the key targets of Sufentanil for the treatment of pulmonary sepsis, and then verified by animal experiments. …”
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