Disproportionality analysis of upadacitinib-related adverse events in inflammatory bowel disease using the FDA adverse event reporting system by Shiyi Wang, Xiaojian Wang, Jing Ding, Xudong Zhang, Hongmei Zhu, Yihong Fan, Changbo Sun

“…This study evaluates upadacitinib-related adverse events (AEs) utilizing data from the US Food and Drug Administration Adverse Event Reporting System (FAERS).MethodsWe employed disproportionality analyses, including the proportional reporting ratio (PRR), reporting odds ratio (ROR), Bayesian confidence propagation neural network (BCPNN), and empirical Bayesian geometric mean (EBGM) algorithms to identify signals of upadacitinib-associated AEs for treating inflammatory bowel disease (IBD).ResultsFrom a total of 7,037,004 adverse event reports sourced from the FAERS database, 37,822 identified upadacitinib as the primary suspect drug in adverse drug events (ADEs), including 1,917 reports specifically related to the treatment of inflammatory bowel disease (IBD). …”
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The role and prognostic value of PANoptosis-related genes in skin cutaneous melanoma by Huijing Feng, Linzi Jia, Yanan Ma, Pengmin Liu, Xiaoling Yang, Lina Hu, Kai Xu, Fan Yang, Dongfeng Zhang, Jian Li, Qi Mei, Fei Han

“…Prognostic genes for SKCM were derived using Cox analysis and machine learning algorithms, leading to the construction and validation of a prognostic model. …”
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Distribution of age at natural menopause, age at menarche, menstrual cycle length, height and BMI in BRCA1 and BRCA2 pathogenic variant carriers and non-carriers: results from EMBR... by Nasim Mavaddat, Debra Frost, Emily Zhao, Daniel R. Barnes, Munaza Ahmed, Julian Barwell, Angela F. Brady, Paul Brennan, Hector Conti, Jackie Cook, Harriet Copeland, Rosemarie Davidson, Alan Donaldson, Emma Douglas, David Gallagher, Rachel Hart, Louise Izatt, Zoe Kemp, Fiona Lalloo, Zosia Miedzybrodzka, Patrick J. Morrison, Jennie E. Murray, Alex Murray, Hannah Musgrave, Claire Searle, Lucy Side, Katie Snape, Vishakha Tripathi, Lisa Walker, Stephanie Archer, D. Gareth Evans, Marc Tischkowitz, Antonis C. Antoniou, Douglas F. Easton

“…Conclusion Information on the distribution of cancer risk factors in PV carriers is needed for incorporating these factors into multifactorial cancer risk prediction algorithms. Contrary to previous reports, we found no evidence that BRCA1 or BRCA2 PV are associated with hormonal or anthropometric factors, except for a weak association with height. …”
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Advances and challenges in immunotherapy in head and neck cancer by Hazem Aboaid, Taimur Khalid, Abbas Hussain, Yin Mon Myat, Rishi Kumar Nanda, Ramaditya Srinivasmurthy, Kevin Nguyen, Daniel Thomas Jones, Jo–Lawrence Bigcas, Kyaw Zin Thein

“…Future research should focus on refining biomarker-driven treatment algorithms, developing rational immunotherapy combinations, and leveraging tumor microenvironment modifications to enhance therapeutic efficacy.…”
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Random forest-driven mortality prediction in critical IBD care: a dual-database model integrating comorbidity patterns and real-time physiometrics by Zhenze Zhang, Zhenze Zhang, Caiqing Zhao, Caiqing Zhao, Yijun Zhou, Ling Yao, Peng Liu, Ziling Fang, Nian Fang, Nian Fang

“…This multicenter study aimed to develop and validate ML-based models for mortality risk stratification in critically ill IBD patients using large-scale ICU databases.MethodsData from 551 IBD patients in the MIMIC-IV database (2008–2019) were analyzed, with external validation using the eICU dataset. Nine ML algorithms (XGBoost, logistic regression, LightGBM, random forest, decision tree, elastic net, MLP, KNN, RSVM) were trained to predict 1-year mortality. …”
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Regional Brain Aging Disparity Index: Region-Specific Brain Aging State Index for Neurodegenerative Diseases and Chronic Disease Specificity by Yutong Wu, Shen Sun, Chen Zhang, Xiangge Ma, Xinyu Zhu, Yanxue Li, Lan Lin, Zhenrong Fu

“…Meanwhile, despite Shapley additive explanations having demonstrated potential for revealing regional heterogeneity, their application in complex deep learning algorithms has been hindered by prohibitive computational complexity. …”
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Data-driven insights into pre-slaughter mortality: Machine learning for predicting high dead on arrival in meat-type ducks by Chalita Jainonthee, Phutsadee Sanwisate, Panneepa Sivapirunthep, Chanporn Chaosap, Raktham Mektrirat, Sudarat Chadsuthi, Veerasak Punyapornwithaya

“…This classification was performed using machine learning (ML) algorithms such as Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine (SVM), Decision Tree (DT), Random Forests (RF), and Extreme Gradient Boosting (XGBoost). …”
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Different strokes: differences in the characteristics and outcomes of BCVI and non-BCVI strokes in trauma patients by Lillian S Kao, Charles E Wade, Xu Zhang, Bryan A Cotton, Sean I Savitz, Michelle K McNutt, Cedar Slovacek, David Rosenbaum, Hari Kishan Reddy Indupuru, John Harvin

“…Blunt cerebrovascular injury (BCVI)–related strokes and mortality have decreased, likely due to refined screening and treatment algorithms in trauma literature; however, there is a paucity of research addressing non-BCVI strokes in trauma. …”
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Remote sensing-based maize growth process parameters revel the maize yield: a comparison of field- and regional-scale by Minghan Cheng, Xiuliang Jin, Chenwei Nie, Kaihua Liu, Tianao Wu, Yuping Lv, Shuaibing Liu, Xun Yu, Yi Bai, Yadong Liu, Lin Meng, Xiao Jia, Yuan Liu, Lili Zhou, Fei Nan

“…However, most previous studies have relied on remote sensing data from one or a few periods for yield estimation, thus lacking a comprehensive description of entire crop growth. Furthermore, past algorithms have not considered their applicability across different observational scales (e.g., unmanned aerial vehicle (UAV)- and satellite-observed). …”
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The most common errors in automatic ECG interpretation by Krzysztof Kraik, Krzysztof Kraik, Irena Anna Dykiert, Irena Anna Dykiert, Joanna Niewiadomska, Joanna Niewiadomska, Marta Ziemer-Szymańska, Karolina Mikołajczak, Karolina Mikołajczak, Mikołaj Kreń, Mikołaj Kreń, Piotr Kukiełka, Piotr Kukiełka, Adrian Martuszewski, Adrian Martuszewski, Tomasz Harych, Rafał Poręba, Paweł Gać, Małgorzata Poręba

“…Future perspectives should include the application of AI in algorithms used in ECG analysis by manufacturers and paying more attention to accessing proper feedback from clinicians to device manufacturers.…”
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Predictive Modeling of Climate-Driven Crop Yield Variability Using DSSAT Towards Sustainable Agriculture by Safa E. El-Mahroug, Ayman A. Suleiman, Mutaz M. Zoubi, Saif Al-Omari, Qusay Y. Abu-Afifeh, Heba F. Al-Jawaldeh, Yazan A. Alta’any, Tariq M. F. Al-Nawaiseh, Nisreen Obeidat, Shahed H. Alsoud, Areen M. Alshoshan, Fayha M. Al-Shibli, Rakad Ta’any

“…Yield projections under each scenario were further analyzed using machine learning algorithms—random forest and gradient boosting regression—to quantify the influence of individual climate variables. …”
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The global distribution and risk prediction of Anaplasmataceae species: a systematic review and geospatial modelling analysisResearch in context by Xiao-Bin Huang, Tian Tang, Jin-Jin Chen, Yuan-Yuan Zhang, Chen-Long Lv, Qiang Xu, Guo-Lin Wang, Ying Zhu, Yue-Hong Wei, Simon I. Hay, Li-Qun Fang, Wei Liu

“…We mapped the richness and global distribution of identified Anaplasmatacea species. Machine learning algorithms were applied to determine the ecological and vector-related factors contributing to the occurrence of major Anaplasmatacea members and project their potential risk distributions. …”
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Comprehensive analysis of phagocytosis regulatory genes in bladder cancer: implications for prognosis and immunotherapy by Xueming Ma, Xueming Ma, Xueming Ma, Dongnuan Yao, Dongnuan Yao, Dongnuan Yao, Weitao Yu, Weitao Yu, Weitao Yu, Gongping Wu, Gongping Wu, Gongping Wu, Chengwei Fan, Chengwei Fan, Chengwei Fan, Junqiang Tian, Junqiang Tian, Junqiang Tian

“…Phagocytosis regulatory genes (PRGs) are involved in regulating the immune response against tumor cells, and in-depth research on them in bladder cancer is extremely urgent.MethodsMulti-omics data from the TCGA and GEO databases were integrated, and strict data preprocessing was carried out. A variety of algorithms and analysis techniques, such as Kaplan-Meier analysis, Cox regression analysis, and ConsensusClusterPlus clustering analysis, were used to identify PRGs related to the prognosis of bladder cancer patients, and functional analysis and clustering analysis were conducted in depth. …”
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Unveiling psychobiological correlates in primary Sjögren’s syndrome: a machine learning approach to determinants of disease burden by László V. Módis, László V. Módis, András Matuz, András Matuz, Zsófia Aradi, Ildikó Fanny Horváth, Antónia Szántó, Antal Bugán

“…Three machine learning algorithms were trained to predict outcome variables, first by each measure category, then on the entire set of predictor variables. …”
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The Value of PET/CT-Based Radiomics in Predicting Adrenal Metastases in Patients with Cancer by Qiujun He, Xiangxing Kong, Xiangxi Meng, Xiuling Shen, Nan Li

“…Based on the selected features, the optimal model was chosen from ten machine learning algorithms, and the nomogram was constructed. <b>Results:</b> The area under the curve (AUC), sensitivity, specificity, and accuracy of conventional parameters of PET/CT were 0.919, 0.849, 0.892, and 0.844, respectively. …”
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Role of inflammation-related genes as prognostic biomarkers and mechanistic implications in idiopathic pulmonary fibrosis by Bing Bai, Bing Bai, Bing Bai, Wenfei Zhao, Fazhan Li, Yang Mi, Pengyuan Zheng

“…Unsupervised clustering algorithms were used to classify IPF samples, followed by weighted gene co-expression network analysis (WGCNA) to identify highly correlated genes. …”
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Empowering cancer research in Europe: the EUCAIM cancer imaging infrastructure by Luis Martí-Bonmatí, Ignacio Blanquer, Manolis Tsiknakis, Gianna Tsakou, Ricard Martinez, Salvador Capella-Gutierrez, Sara Zullino, Janos Meszaros, Esther E. Bron, Jose Luis Gelpi, Katrine Riklund, Linda Chaabane, Heinz-Peter Schlemmer, Mario Aznar, Patricia Serrano Candelas, Peter Gordebeke, Monika Hierath, the EUCAIM Consortium, European Society of Radiology

“…However, the development of new AI algorithms requires access to large and complex real-world datasets. …”
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Closing the AI generalisation gap by adjusting for dermatology condition distribution differences across clinical settingsResearch in context by Rajeev V. Rikhye, Aaron Loh, Grace Eunhae Hong, Preeti Singh, Margaret Ann Smith, Vijaytha Muralidharan, Doris Wong, Rory Sayres, Ayush Jain, Michelle Phung, Nicolas Betancourt, Bradley Fong, Rachna Sahasrabudhe, Khoban Nasim, Alec Eschholz, Basil Mustafa, Jan Freyberg, Terry Spitz, Yossi Matias, Greg S. Corrado, Katherine Chou, Dale R. Webster, Peggy Bui, Yuan Liu, Yun Liu, Justin Ko, Steven Lin

“…Interpretation: AI algorithms can be efficiently adapted to new settings without additional training data by recalibrating the existing model, or with targeted data acquisition for rarer conditions and retraining just the final layer. …”
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Effectiveness evaluation of the complex medical and psychological method of treating patients with insomnia by A. V. Vasil'eva, T. A. Karavaeva, Yu. P. Kolesova, D. S. Radionov, D. A. Starunskaya, M. V. Fomicheva, S. M. Abdullaeva

“…The uniqueness of the developed program lies in its clear algorithmization, the absence of complex techniques of psychological (psychotherapeutic) intervention, which ensures the reproducibility of the program in practical health care institutions if there is a psychotherapist or clinical psychologist on staff who knows the skills of cognitive behavioral therapy and diaphragmatic breathing. …”
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Deciphering the impact of intra-tumoral bacterial infiltration on multi-omics profiles in low-grade gliomas by Wenshu Li, Zixiang Zhu, Zixiang Zhu, Longyuan Li, Xin Wu, Jiaxuan Li, Yi Zhou, Lingwen Gu, Pranathi Vittal, Zhouqing Chen, Zhong Wang, Lingchuan Guo

“…For the TCGA samples, utilizing advanced machine learning algorithms, this study identified distinct patterns of bacterial infiltration within the LGG population and constructed a prognostically relevant intra-tumoral bacteria risk model (PRIBR Index). …”
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