Prognostic value of multi-PLD ASL radiomics in acute ischemic stroke by Zhenyu Wang, Yuan Shen, Xianxian Zhang, Qingqing Li, Congsong Dong, Shu Wang, Haihua Sun, Mingzhu Chen, Xiaolu Xu, Pinglei Pan, Pinglei Pan, Zhenyu Dai, Fei Chen, Fei Chen

“…Features were selected using least absolute shrinkage and selection operator regression, and three models were developed: a clinical model, a CBF radiomics model, and a combined model, employing eight ML algorithms. Model performance was assessed using receiver operating characteristic curves and decision curve analysis (DCA). …”
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Multilevel determinants of racial/ethnic disparities in severe maternal morbidity and mortality in the context of the COVID-19 pandemic in the USA: protocol for a concurrent triang... by Bankole Olatosi, Jiajia Zhang, Xiaoming Li, Chen Liang, Jihong Liu, Peiyin Hung, Shan Qiao, Berry A Campbell, Myriam E Torres, Neset Hikmet

“…Non-Hispanic black and Hispanic pregnant women appear to have disproportionate SARS-CoV-2 infection and death rates.Methods and analysis We will use the socioecological framework and employ a concurrent triangulation, mixed-methods study design to achieve three specific aims: (1) examine the impacts of the COVID-19 pandemic on racial/ethnic disparities in severe maternal morbidity and mortality (SMMM); (2) explore how social contexts (eg, racial/ethnic residential segregation) have contributed to the widening of racial/ethnic disparities in SMMM during the pandemic and identify distinct mediating pathways through maternity care and mental health; and (3) determine the role of social contextual factors on racial/ethnic disparities in pregnancy-related morbidities using machine learning algorithms. We will leverage an existing South Carolina COVID-19 Cohort by creating a pregnancy cohort that links COVID-19 testing data, electronic health records (EHRs), vital records data, healthcare utilisation data and billing data for all births in South Carolina (SC) between 2018 and 2021 (>200 000 births). …”
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Risk factors and prediction model of breast cancer-related lymphoedema in a Chinese cancer centre: a prospective cohort study protocol by Yue Wang, Xin Li, Ying Wang, Hongmei Zhao, Qian Lu, Yujie Zhou, Liyuan Zhang, Aomei Shen, Jingru Bian, Wanmin Qiang, Jingming Ye, Hongmeng Zhao, Yubei Huang, Zhongning Zhang, Peipei Wu

“…Traditional COX regression analysis and seven common survival analysis machine learning algorithms (COX, CARST, RSF, GBSM, XGBS, SSVM and SANN) will be employed for model construction and validation.Ethics and dissemination The study protocol was approved by the Biomedical Ethics Committee of Peking University (IRB00001052-21124) and the Research Ethics Committee of Tianjin Medical University Cancer Institute and Hospital (bc2023013). …”
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MASFNet: Multi-level attention and spatial sampling fusion network for pine wilt disease trees detection by Dong Ren, Meng Li, Ziyu Hong, Li Liu, Jingfeng Huang, Hang Sun, Shun Ren, Pan Sao, Wenbin Wang, Jingcheng Zhang

“…However, due to the diversity of object information in UAV remote sensing images, most existing algorithms are prone to confusing the background environment and difficult to distinguish highly similar ground objects, resulting in a lot of false detections. …”
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Predicting the risk of heart failure after acute myocardial infarction using an interpretable machine learning model by Qingqing Lin, Qingqing Lin, Wenxiang Zhao, Wenxiang Zhao, Hailin Zhang, Hailin Zhang, Wenhao Chen, Sheng Lian, Qinyun Ruan, Qinyun Ruan, Zhaoyang Qu, Zhaoyang Qu, Yimin Lin, Yimin Lin, Dajun Chai, Dajun Chai, Dajun Chai, Dajun Chai, Xiaoyan Lin, Xiaoyan Lin, Xiaoyan Lin, Xiaoyan Lin

“…For developing a predictive model for HF risk in AMI patients, the least absolute shrinkage and selection operator (LASSO) Regression was used to feature selection, and four ML algorithms including Random Forest (RF), Extreme Gradient Boost (XGBoost), Support Vector Machine (SVM), and Logistic Regression (LR) were employed to develop the model on the training set. …”
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Petrological controls on the engineering properties of carbonate aggregates through a machine learning approach by Javid Hussain, Tehseen Zafar, Xiaodong Fu, Nafees Ali, Jian Chen, Fabrizio Frontalini, Jabir Hussain, Xiao Lina, George Kontakiotis, Olga Koumoutsakou

“…Among these, the Gradient Boosting model demonstrated superior predictive capability, overcoming both traditional regression methods and other machine learning algorithms as validated through the Taylor diagram and ranking system (i.e., r = 0.998, R² = 997, Root mean square error = 0.075, Variance Accounted For = 99.50%, Mean Absolute Percentage Error = 0.385%, Alpha 20 Index = 100, and performance index = 0.975). …”
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Neuromorphic, physics-informed spiking neural network for molecular dynamics by Vuong Van Pham, Temoor Muther, Amirmasoud Kalantari Dahaghi

“…It also leverages the enhanced representation of real biological neural systems through spiking neural network integration with molecular dynamic physical principles, offering greater efficiency compared to conventional AI algorithms. NP-SNN integrates three core components: (1) embedding MD principles directly into the training, (2) employing best practices for training physics-informed ML systems, and (3) utilizing a highly advanced and efficient SNN architecture. …”
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A novel gene signature for predicting outcome in colorectal cancer patients based on tumor cell-endothelial cell interaction via single-cell sequencing and machine learning by Lina Pang, Qingxia Sun, Wenyue Wang, Mingjie Song, Ying Wu, Xin Shi, Xiaonan Shi

“…Prognostic signatures were developed using various machine learning algorithms based on marker genes linked to the identified cell subpopulations. …”
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Identification and validation of glycolysis-related diagnostic signatures in diabetic nephropathy: a study based on integrative machine learning and single-cell sequence by Xiaoyin Wu, Xiaoyin Wu, Buyu Guo, Buyu Guo, Xingyu Chang, Xingyu Chang, Yuxuan Yang, Yuxuan Yang, Qianqian Liu, Qianqian Liu, Jiahui Liu, Jiahui Liu, Yichen Yang, Yichen Yang, Kang Zhang, Yumei Ma, Songbo Fu, Songbo Fu, Songbo Fu

“…The expression levels of diagnostic signatures were verified in vitro.ResultsThrough the 108 combinations of machine learning algorithms, we selected 12 diagnostic signatures, including CD163, CYBB, ELF3, FCN1, PROM1, GPR65, LCN2, LTF, S100A4, SOX4, TGFB1 and TNFAIP8. …”
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Serum metabolome associated with novel and legacy per- and polyfluoroalkyl substances exposure and thyroid cancer risk: A multi-module integrated analysis based on machine learning by Fei Wang, Yuanxin Lin, Lian Qin, Xiangtai Zeng, Hancheng Jiang, Yanlan Liang, Shifeng Wen, Xiangzhi Li, Shiping Huang, Chunxiang Li, Xiaoyu Luo, Xiaobo Yang

“…PFHxA and PFDoA exposure associated with increased TC risk, while PFHxS and PFOA associated with decreased TC risk in single compound models (all P < 0.05). Machine learning algorithms identified PFHxA, PFDoA, PFHxS, PFOA, and PFHpA as the key PFAS influencing the development of TC, and mixed exposures have an overall positive effect on TC risk, with PFHxA making the primary contribution. …”
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Enhancing individual glomerular filtration rate assessment: can we trust the equation? Development and validation of machine learning models to assess the trustworthiness of estima... by Antoine Lanot, Anna Akesson, Felipe Kenji Nakano, Celine Vens, Jonas Björk, Ulf Nyman, Anders Grubb, Per-Ola Sundin, Björn O. Eriksen, Toralf Melsom, Andrew D. Rule, Ulla Berg, Karin Littmann, Kajsa Åsling-Monemi, Magnus Hansson, Anders Larsson, Marie Courbebaisse, Laurence Dubourg, Lionel Couzi, Francois Gaillard, Cyril Garrouste, Lola Jacquemont, Nassim Kamar, Christophe Legendre, Lionel Rostaing, Natalie Ebert, Elke Schaeffner, Arend Bökenkamp, Christophe Mariat, Hans Pottel, Pierre Delanaye

“…Four machine learning and two traditional logistic regression models were trained on a cohort of 9,202 participants to predict the likelihood of the EKFC creatinine-derived eGFR falling within 30% (p30), 20% (p20) or 10% (p10) of the mGFR value. The algorithms were internally and then externally validated on cohorts of respectively 3,034 and 10,107 participants. …”
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Trends, outcomes and knowledge gaps in mobile apps for reproductive endocrinology and infertility: a scoping review protocol by Alba Regina de Abreu Lima, Emerson Roberto dos Santos, Aline Russomano de Gouvêa, Natália Almeida de Arnaldo Silva Rodriguez Castro, João Daniel de Souza Menezes, Matheus Querino da Silva, Helena Landin Gonçalves Cristóvão, Cíntia Canato Martins, Jéssica Gisleine de Oliveira, Patrícia da Silva Fucuta, Alexandre Lins Werneck, Gerardo Maria de Araújo Filho, Heloisa Cristina Caldas, Vânia Maria Sabadoto Brienze, Júlio César André, Antônio Hélio Oliani

“…Despite promising advancements such as the development of apps with sophisticated algorithms for ovulation prediction and comprehensive platforms offering integrated fertility education and emotional support, there remain gaps in the literature regarding the comprehensive evaluation of mobile apps for reproductive endocrinology and infertility. …”
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Analysis of mutations in CDC27, CTBP2, HYDIN and KMT5A genes in carotid paragangliomas by E. N. Lukyanova, A. V. Snezhkina, D. V. Kalinin, A. V. Pokrovsky, A. L. Golovyuk, O. A. Stepanov, E. A. Pudova, G. S. Razmakhaev, M. V. Orlova, A. P. Polyakov, M. V. Kiseleva, A. D. Kaprin, A. V. Kudryavtseva

“…Using several prediction algorithms (SIFT, PolyPhen-2, MutationTaster, and LRT), potentially pathogenic mutations were identified in four genes (CDC27, CTBP2, HYDIN, and KMT5A). …”
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Deciphering the role of metal ion transport-related genes in T2D pathogenesis and immune cell infiltration via scRNA-seq and machine learning by Zuhui Pu, Zuhui Pu, Tony Bowei Wang, Ying Lu, Ying Lu, Zijing Wu, Zijing Wu, Yuxian Chen, Ziqi Luo, Xinyu Wang, Lisha Mou, Lisha Mou

“…We employed 12 machine learning algorithms to develop predictive models and assessed immune cell infiltration using single-sample gene set enrichment analysis (ssGSEA). …”
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Deep learning captures the effect of epistasis in multifactorial diseases by Vladislav Perelygin, Alexey Kamelin, Alexey Kamelin, Nikita Syzrantsev, Layal Shaheen, Layal Shaheen, Anna Kim, Nikolay Plotnikov, Anna Ilinskaya, Valery Ilinsky, Alexander Rakitko, Alexander Rakitko, Maria Poptsova

“…The aim of the presented study is to explore the power of non-linear machine learning algorithms and deep learning models to predict the risk of multifactorial diseases with epistasis.MethodsSimulated data with 2- and 3-loci interactions and tested three different models of epistasis: additive, multiplicative and threshold, were generated using the GAMETES. …”
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Correlation between the white blood cell/platelet ratio and 28-day all-cause mortality in cardiac arrest patients: a retrospective cohort study based on machine learning by Huai Huang, Guangqin Ren, Shanghui Sun, Zhi Li, Yongtian Zheng, Lijuan Dong, Shaoliang Zhu, Xiaosheng Zhu, Wenyu Jiang

“…ObjectiveThis study aims to evaluate the association between the white blood cell-to-platelet ratio (WPR) and 28-day all-cause mortality among patients experiencing cardiac arrest.MethodsUtilizing data from 748 cardiac arrest patients in the Medical Information Mart for Intensive Care-IV (MIMIC-IV) 2.2 database, machine learning algorithms, including the Boruta feature selection method, random forest modeling, and SHAP value analysis, were applied to identify significant prognostic biomarkers. …”
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Stage actor tracking method based on infrared ink marking(基于红外油墨标记的舞台演员跟踪算法) by 李平(LI Ping), 陈书界(CHEN Shujie), 王登辉(WANG Denghui), 刘钟淋(LIU Zhonglin), 王勋(WANG Xun), 周迪(ZHOU Di), 丁勇(DING Yong)

“…When well illuminated, the state-of-the-art multi-object tracking algorithms can reach real-time and stable tracking results. …”
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Children with autoimmune hepatitis receiving standard-of-care therapy demonstrate long-term obesity and linear growth delay by Or Steg Saban, Shannon M. Vandriel, Syeda Aiman Fatima, Celine Bourdon, Amrita Mundh, Vicky L. Ng, Simon C. Ling, Robert H.J. Bandsma, Binita M. Kamath

“…These data indicate the need to re-evaluate standard treatment algorithms for pediatric AIH in terms of steroid dosing and potential nonsteroid alternatives.…”
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A new signature associated with anoikis predicts the outcome and immune infiltration in nasopharyngeal carcinoma by Yonglin Luo, Wenyang Wei, Yaxuan Huang, Jun Li, Weiling Qin, Quanxiang Hao, Jiemei Ye, Zhe Zhang, Yushan Liang, Xue Xiao, Yonglin Cai

“…Results Three differentially expressed ARGs (CDC25C, E2F1 and RBL2) with prognostic value were identified by the intersection of multiple machine learning algorithms. A risk score based on t 3-ARG feature was developed to stratify NPC patients into two distinct risk groups using the optimal model, Random Survival Forest. …”
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Combining UAV Remote Sensing with Ensemble Learning to Monitor Leaf Nitrogen Content in Custard Apple (<i>Annona squamosa</i> L.) by Xiangtai Jiang, Lutao Gao, Xingang Xu, Wenbiao Wu, Guijun Yang, Yang Meng, Haikuan Feng, Yafeng Li, Hanyu Xue, Tianen Chen

“…This study uses an ensemble learning technique based on multiple machine learning algorithms to effectively and precisely monitor the leaf nitrogen content in the tree canopy using multispectral canopy footage of custard apple trees taken via Unmanned Aerial Vehicle (UAV) across different growth phases. …”
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