Differential chemoproteomic analysis of RRS-1 candidate molecule and molecules of several nonsteroidal anti-inflammatory drugs by P. A. Galenko-Yaroshevsky, I. Yu. Torshin, A. N. Gromov, O. A. Gromova, K. F. Suzdalev, R. A. Murashko, A. V. Zelenskaya, A. V. Zadorozhniy, T. R. Glechyan, G. V. Simavonyan, E.M. I. Muhammad

“…To plan effective and safe pharmacotherapy for inflammation and pain, it is important to evaluate the mechanisms and spectrum of action of nonsteroidal anti-inflammatory drugs (NSAIDs), including their effects on human proteome.Objective: to identify and evaluate the most significant specific differences of candidate molecule RRS-1 (N-{(Z)-2-(1-methyl-1H-indol-3-yl)1-[(propylamino)carbonyl]vinyl}benzamide) from other NSAIDs through differential chemoreactome analysis.Material and methods. …”
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Optimization of Traffic Flow by Allowing Private Cars to Merge into the Bus Lane at Specific Locations by Zhentao Yin, Kai Yuan, Shenshen Zheng

“…We use the genetic algorithms to optimize the system. Finally, simulation results show that the partially space-shared bus lane reduces private-car delays and total delays of the system. …”
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Safety assessment of sodium zirconium cyclosilicate: A FAERS-based disproportionality analysis. by Yongfei Yu, Kaiyu Zhang, Jinglin Gao, Guoshun Huang, Chen Yong, Yuan Wei, Enchao Zhou

“…In this study, we used the Food and Drug Administration Adverse Event Reporting System (FAERS) database to evaluate the safety profilet of SZC. This retrospective analysis involved FAERS data from the third quarter of 2018 to the fourth quarter of 2023. …”
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ROBO-ADVISORS AND AI-DRIVEN FUNDS: CATALYSTS IN THE DYNAMIC EVOLUTION OF ASSET MANAGEMENT by Amirul Ammar Anuar, Mohammad Taqiuddin Mohamad, Ahmad Azam Sulaiman @ Mohamad

“…Robo-advisors automate investor profiling and fund recommendations, whereas AI-driven funds use algorithms for autonomous trading decisions. AI adoption varies among asset management companies (AMCs), with differing levels of integration. …”
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Socio-Economical Analysis of a Green Reverse Logistics Network under Uncertainty: A Case Study of Hospital Constructions by Alireza Alibakhshi, Amirreza Saffarian, Erfan Hassannayebi

“…It suggests potential future directions, such as the application of metaheuristic algorithms and improved stochastic planning methods.…”
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Hindcasting Maximum Water Depths in Coastal Watersheds: The Importance of Incorporating Off‐Channel Data and Their Uncertainties in Machine Learning Models by Maryam Pakdehi, Ebrahim Ahmadisharaf

“…To answer these questions, we evaluated the performance of ML models across three large coastal watersheds in the US during three hurricanes—Michael, Ida and Ian. …”
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RETRACTED ARTICLE: Molecular mechanism and diagnostic marker investigation of endoplasmic reticulum stress on periodontitis by Qianqian Sun, Enqiang Zhu

“…Two machine learning algorithms were used to reveal potential ERS diagnostic markers of periodontitis. …”
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ADF-SL: An Adaptive and Fair Scheme for Smart Learning Task Distribution by Ahmed A. Al-Saedi, Veselka Boeva

“…This can result in significant model divergence and decreased performance due to differences in data distributions between various clients. …”
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Tirzepatide safety in type 2 diabetes: a disproportionality analysis of adverse events using the FDA FAERS database by Zhenpo Zhang, Jiangxiong Li, Jingping Zheng, Yankun Liang, Lin Ma, Ling Su

“…This study evaluated adverse events reported with tirzepatide, a dual GIP/GLP-1 receptor agonist approved for type 2 diabetes and obesity, using real-world data from the FDA Adverse Event Reporting System. …”
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Comparison of dose calculation approaches and clinical dose–response in image-guided head&neck brachytherapy of the oral cavity by Sabrina Schaller, Vratislav Strnad, Claudia Schweizer, Dorota Lubgan, Ricarda Merten, Rainer Fietkau, Christoph Bert, Andre Karius

“…All clinical, TG-43 based treatment plans were re-calculated using a MBDCA integrated into our planning system. Differences considering several target volume, tissue, and bone dose parameters were evaluated. …”
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Establishing a clinical prediction model for diabetic foot ulcers in type 2 diabetic patients with lower extremity arteriosclerotic occlusion using machine learning by Yubo Wang, Chunyu Jiang, Yi qi Xing, Linxuan Zou, Mingzhi Song, Xueling Qu, Zhuqiang Jia, Lin Zhao, Xin Han, Junwei Zong, Shouyu Wang

“…Intergroup comparisons were conducted to analyze the differences between these two groups. Logistic regression analyses, 3 kinds of machine learning algorithms, a predictive model and nomogram was formulated to estimate the risk of DFU occurrence among diabetic patients with lower extremity ASO. …”
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Changing the Paradigm for Tractography Segmentation in Neurosurgery: Validation of a Streamline-Based Approach by Silvio Sarubbo, Laura Vavassori, Luca Zigiotto, Francesco Corsini, Luciano Annicchiarico, Umberto Rozzanigo, Paolo Avesani

“…A paired <i>t</i>-test was performed on the irregularity measurement to compare segmentations achieved with the two approaches. Qualitative differences were evaluated through visual inspection. …”
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Spectral divergence prioritizes key classes, genes, and pathways shared between substance use disorders and cardiovascular disease by Everest Castaneda, Everest Castaneda, Elissa Chesler, Erich Baker

“…By adding a connectivity-based method, network rankings of similarity and relationships are explored between classes of SUDs and CVD.MethodsGraph spectral clustering's utility is evaluated relative to commonly used network algorithms for discernment between two distinct co-occurring disorders and capacity to rank pathways based on their distinctiveness. …”
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Validation of Sea Surface Temperature From GK-2A Geostationary Satellite and Error Reduction Considering Impact of Satellite Zenith Angle by Kyung-Ae Park, Hye-Jin Woo, Stephane Saux Picart, Anne O'Carroll, Eunha Sohn, HuiTae Joo, Joon-Soo Lee, Joon-Yong Yang

“…This study evaluates the accuracy of sea surface temperature (SST) data produced by Korea's second geostationary satellite, GK-2A, over its first four years of operation (2019&#x2013;2023). …”
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Comparing Short-Term Outcomes of Ventral Hernia Repair Using Heavyweight Non-Woven Polypropylene Mesh With Heavyweight Knitted Polypropylene Mesh by Aldo Fafaj, Lucas R. A. Beffa, Clayton C. Petro, Ajita S. Prabhu, Benjamin T. Miller, Li-Ching Huang, Ryan C. Ellis, Sara M. Maskal, Nir Messer, Sergio Mazzola Poli de Figueiredo, Michael J. Rosen

“…A propensity score model and matching algorithms were implemented to address potential treatment-choice bias. …”
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AI Efficiency in Dentistry: Comparing Artificial Intelligence Systems with Human Practitioners in Assessing Several Periodontal Parameters by Oana-Maria Butnaru, Monica Tatarciuc, Ionut Luchian, Teona Tudorici, Carina Balcos, Dana Gabriela Budala, Ana Sirghe, Dragos Ioan Virvescu, Danisia Haba

“…<i>Backgrounds and Objectives:</i> This study aimed to evaluate the reliability of AI-assisted dental–periodontal diagnoses compared to diagnoses made by senior specialists, specialists, and general dentists. …”
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Predicting nosocomial pneumonia of patients with acute brain injury in intensive care unit using machine-learning models by Junchen Pan, Zhen Yue, Jing Ji, Yongping You, Liqing Bi, Yun Liu, Xinglin Xiong, Genying Gu, Ming Chen, Shen Zhang

“…The training set revealed the superior and robust performance of the XGBoost with the highest AUC value (0.956), while the Random Forest and Adaptive Boost had the highest AUC value (0.883) in validation set.ConclusionMachine learning models can effectively predict the risk of nosocomial pneumonia infection in patients with acute brain injury in the ICU. Despite differences in populations and algorithms, the models we constructed demonstrated reliable predictive performance.…”
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Artificial Intelligence–Enabled ECG Screening for LVSD in LBBB by Hak Seung Lee, MD, Sooyeon Lee, MD, Sora Kang, MS, Ga In Han, MS, Ah-Hyun Yoo, MS, Jong-Hwan Jang, PhD, Yong-Yeon Jo, PhD, Jeong Min Son, MD, Min Sung Lee, MD, MS, Joon-myoung Kwon, MD, MS, Kyung-Hee Kim, MD, PhD

“…Conclusions: Our findings indicate that a broad AI-ECG model reliably detects LVSD in LBBB patients, and transfer learning offers modest improvements without requiring curated LBBB data sets. Evaluating algorithms in representative clinical populations is essential.…”
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Machine learning-based diagnostic model of lymphatics-associated genes for new therapeutic target analysis in intervertebral disc degeneration by Maoqiang Lin, Maoqiang Lin, Shaolong Li, Yabin Wang, Yabin Wang, Guan Zheng, Guan Zheng, Fukang Hu, Fukang Hu, Qiang Zhang, Qiang Zhang, Pengjie Song, Haiyu Zhou, Haiyu Zhou

“…Subsequently, four machine learning algorithms (SVM-RFE, Random Forest, XGB, and GLM) were used to select the method to construct the diagnostic model. …”
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SpaGAN: A spatially-aware generative adversarial network for building generalization in image maps by Zhiyong Zhou, Cheng Fu, Robert Weibel

“…It takes a representative cGAN, pix2pix, as the backbone, and modifies two modules: In the U-Net-based generator, an atrous spatial pyramid pooling (ASPP) module replaces the conventional convolutional module to extract multi-scale features of buildings of varying sizes and shapes; in the PatchGAN-based discriminator, a signed distance map (SDM) module is used to capture the fine-grained shape difference for discrimination. The proposed network was comprehensively evaluated with a synthetic and a real-world dataset. …”
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